DEV Community: Codacy

Fun Open-Source Tools to Check Out in 2026

Codacy — Fri, 13 Nov 2026 15:02:21 +0000

We at Codacy are all about open-source tools.

We run dozens of them for your code quality and security convenience. But there’s a much wider world of code scanners out there than we’ve managed to integrate into Codacy so far!

So, we thought we’d drop a note with a few fun tools we’ve been messing around with here.

ZAP

ZAP (Zed Attack Proxy) is a free, open-source tool for testing running web applications for security vulnerabilities. You may know it as OWASP ZAP, but it hasn’t been an OWASP project since August 2023. In September 2024, its project leaders joined Checkmarx, and it’s now branded “ZAP by Checkmarx”. It’s still open source under the Apache 2.0 license and still run by the ZAP Core Team.

You can send your ZAP reports from your CI/CD into Codacy to get your DAST results alongside your SAST, IaC, secrets, SCA, and penetration testing results in Security and risk management. Business-tier customers can also run ZAP-powered app scans from within Codacy itself.

Outside Codacy, you can run ZAP from its desktop app, or from your CI/CD using its Docker images and packaged baseline, full, and API scans.

GitDiagram

GitDiagram takes a GitHub repository and uses AI to draw an interactive architecture diagram of it. Utterly ingenious. A handy shortcut: replace “hub” with “diagram” in any GitHub URL.

It also handles private repos (with a GitHub token), exports to PNG or Mermaid, has an MCP server for AI agents, and can now make one-minute narrated explainer videos (generating new ones is in early access). It’s MIT-licensed (repo).

We’ve tested it out on a couple of sample repos, including codacy-coverage-reporter, our open-source tool for posting code coverage results to us. Its diagram does a decent job of laying out what’s going on.

OpenSSF Scorecard

The 2025 edition of this list featured Legitify, which scans GitHub and GitLab organizations for policy violations. Its repository hasn’t had a commit since September 2024 or a release since July 2024, so this year we’re pointing you to OpenSSF Scorecard instead.

Scorecard runs automated checks against a GitHub or GitLab repository and scores each from 0 to 10: branch protection, code review, dangerous GitHub Actions workflow patterns, token permissions, pinned dependencies, known vulnerabilities, and more. Run it from the command line or as a GitHub Action, or look up public projects in its web viewer.

export GITHUB_AUTH_TOKEN=<your token>
scorecard --repo=github.com/<org>/<repo>

Like Legitify, it doesn’t bomb out if your token lacks some permissions: without an admin token, it bases its branch protection results on the settings a non-admin token can see. It doesn’t check 2FA, as GitHub and GitLab don’t make that data public. To enforce policies across a whole GitHub organization, look at the OpenSSF’s Allstar app.

Infracost

On the DevOps side, Infracost estimates cloud costs from your infrastructure as code (Terraform, Terragrunt, CloudFormation, and AWS CDK). When you make a pull request (PR) with infra changes, it lays out the projected cost changes, along with any FinOps policy checks, right there in the PR.

We love this because it is so close to how Codacy operates. It gives you high-value feedback when and where you are interested rather than hiding it in some third-party UI.

Infracost changed shape with its 2.0 release: the Apache 2.0-licensed CLI now lives in its own repository, alongside editor extensions and skills for AI coding agents. You need to log in to an Infracost account to use the CLI, and Infracost Cloud, the dashboard for team-wide policies, is a separate SaaS product.

Google Lighthouse

Google Lighthouse is an open-source page auditor with audits for performance, accessibility, best practices, and SEO. You can run it from the Lighthouse panel in Chrome DevTools, from the command line, as a Node module in CI, or through PageSpeed Insights.

Its accessibility audits matter more than ever in Europe, where the EU’s European Accessibility Act has applied since 28 June 2025. It sets accessibility requirements for a range of products and services, such as e-commerce, banking services, and e-books, including the websites and mobile apps used to provide them.

Lighthouse leverages axe-core, an open-source accessibility testing engine, behind the scenes. You get both scoring on a load of metrics and actionable findings that you can remediate to improve, particularly in the accessibility space.

If you’re interested in accessibility, by the way, Codacy includes dozens of accessibility rules for JavaScript and TypeScript through ESLint plugins: jsx-a11y for React, vuejs-accessibility for Vue, and react-native-a11y for React Native. To find them, use the “Discover patterns” button on your repository’s Code patterns page and search for “a11y” or “accessibility”.

Hope some of that piqued your interest!

What Is AI Context Engineering? Giving Coding Agents the Right Context

Codacy — Thu, 12 Nov 2026 09:54:57 +0000

TL;DR

In most team setups, security findings block merges, while code quality remains advisory. Duplication and rising complexity don't fail a build, and agent-generated code adds more redundancy per change than human-written code. Codacy plugs into your existing harness as a deterministic check, returning the exact file and line of each issue so the agent fixes it before a human reviews the pull request.

When a coding agent breaks a rule your team agreed on, the usual instinct is to give it more instructions, but more material can make the agent worse. Chroma tested 18 models and found performance dropped as input length grew, even when the task stayed the same.

The alternative is AI context engineering: deciding what a coding agent knows at each step, from architecture and conventions to past decisions. Because several engineers and agents now share the same files, keeping that context accurate needs an owner.

In this article, we'll cover why more context isn't enough, which kinds of context an agent needs, what happens when it goes stale, and what your team should own versus what tooling should handle.

What does context engineering actually mean for AI coding agents?

Context engineering means deciding what information an agent sees before its next action, and maintaining that information with the same discipline a team applies to the code itself.

Andrej Karpathy popularized the term over prompt engineering in mid-2025, posting that he preferred "context engineering" over "prompt engineering" because people associate prompts with short task descriptions you'd give an LLM in day-to-day use.

Instead, he described context engineering as "the delicate art and science of filling the context window with just the right information for the next step."

For a coding agent, context is never just the prompt typed into a chat window. It is the assembled set of files, instructions, standards, history, and check results the agent is handed for that specific step.

Some of that material gets built automatically for a single task: the open diff, the ticket, the files touched last.

Other material persists across tasks and across engineers, and nobody assembles it automatically.

Inside our loop, graph, and harness framing:

The graph decides order and routing.
The harness decides what the agent can touch and what checks its output.
The loop decides when to retry or stop.
Context engineering is the fourth layer, deciding what the agent knows at each step, and it lives inside the harness.

Why just giving the agent more context isn't enough anymore?

Chroma's report also shows what works. Every model scored significantly higher on a focused input of about 300 tokens than on the same facts buried in about 113,000 tokens of chat history. Distractors did the most damage: related but wrong content lowered accuracy, and the effect grew with input length.

A stale convention sitting next to a current one confuses the agent: it has no reliable way to tell which one wins, and the stale rule competes for attention with the guidance that matters.

For teams running agents against a shared codebase, the real work is choosing which material earns a place in the window for this step and discarding the rest.

What kinds of context does an AI engineering agent need?

An AI engineering agent needs several distinct kinds of context to produce a usable first attempt, and each comes from a different source inside the repository and the team's history.

Here's what each one covers:

Architecture: how the system is structured and why certain boundaries exist. This comes from architecture notes and design decisions written into the repository, so the agent doesn't have to guess structure from file names.
Conventions: naming, patterns, and house style. This lives in coding standards and instruction files, and it is the layer most teams already maintain, even if only informally.
Constraints: security rules, dependency policies, and performance limits that must hold regardless of what the agent proposes. These start as written policy, but they only matter in practice once an enforced check backs them.
Past decisions: what the team tried, what broke, and what it decided to do differently. Instruction files capture this poorly unless someone writes the lesson down the day it happens, often as a rule dated to the incident that caused it.
Feedback signals: the results that tell the agent when it got something wrong. These come out of the harness as check output and only become useful once fed back into the next attempt.

Most of this is team-level: shared, persistent, and valuable only if someone keeps it current.

Constraints and feedback signals hold up better as check results than as prose in a Markdown file, because a deterministic check gives the same answer every run. For example, the Codacy MCP Server and Codacy Analysis CLI let an agent run Codacy's analysis locally and read each finding's exact file and line.

What happens when context goes stale, contradicts itself, or diverges across repos?

A harness delivers context to an agent faithfully, whether or not that context is still correct, and a stale rule reaches the agent with exactly the same authority as a current one. It has no way to know the instruction file it is loading describes a decision the team reversed months earlier, so it passes the outdated rule along with full confidence.

The failure compounds when the agent checks its own output. It evaluates the change against the same instruction file that shaped it, so it passes its own review, just as a developer can't catch a misread requirement by rereading the ticket that caused the misreading.

The problem grows across repositories. When one team updates its instruction file after an incident, and another never does, the same change can pass in one repository and get flagged in another, depending on which file sits in that directory.

Verification has to come from somewhere independent of the context that shaped the work. That challenge is quite common: Codacy's scan of 34,266 repositories found gaps in AI agent configuration files in one in four organizations.

Context as a maintained artifact

Team-level context is an artifact with the same lifecycle needs as code: it needs an owner, versioning, and updates on a cadence, or it drifts the way any unmaintained module would.

Teams without a dedicated platform function tend to skip this by default, because instruction files feel like documentation rather than infrastructure, and documentation is usually the first thing cut when a sprint runs long.

Without an owner, every agent inherits whatever state the instruction files happen to be in.

One habit helps. In our own agent setup, each instruction-file rule carries the date something went wrong, so the same mistake doesn't resurface on the next run.

The habit works for one engineer. It breaks down as a team grows because it depends on someone remembering to write the rule, someone pruning the ones that are no longer true, and every repository getting the same update.

That's a lot to rely on memory when more than one in five code reviews on GitHub now involve an agent. Context that keeps up has to be captured as decisions are made and retired when it stops applying, without anyone maintaining it by hand.

Build vs. buy: what should teams own, and what should tooling handle?

Teams own the knowledge of what is true about their system. Tooling should keep that knowledge current and enforce what is allowed to merge. Confusing the two is how an optional check quietly becomes the only check a team has.

An instruction-file rule is a request the agent may or may not follow. A check that runs on every change is a guarantee, and an optional check tends to get skipped exactly when it matters.

In practice, the split looks like this:

Own yourself: the decisions and the reasons behind them, such as architecture boundaries, conventions, and what the team learned from an incident. No outside tool can know why your system is shaped the way it is.
Let tooling keep the record: capturing those decisions as they happen, keeping them consistent across repositories, and retiring the ones that stop applying, so nobody updates instruction files by hand.
Let tooling enforce: the deterministic, independent checks that decide whether a change is allowed to merge, regardless of what any instruction file claims about the rule.

How does context quality affect agent output, review load, and risk?

Better team-level context improves an agent's first attempt, and the effect compounds across every agent drawing on the same files. Accurate architecture notes and current conventions mean fewer rounds of correction, which matters because AI time savings often shift to review without it.

Independent checks reduce review load from the other side: they catch what the context missed or got wrong before the change reaches a human, so the reviewer looks at work that has already cleared the mechanical issues.

Both effects push in the same direction, lowering what slips through to merge and lowering review time per change as a team grows.

This also shapes how much engineers actually trust what an agent produces. Stack Overflow's 2026 developer survey found that 48% of developers trust AI output when they can easily validate the answers, while only 16% extend that trust to tasks involving important work decisions.

Independent verification is what makes that validation cheap enough to run on every change rather than only the ones a reviewer happens to have time for.

Where Codacy fits: preserving knowledge and verifying the result

Verity, Codacy's second product, is a pair programmer for coding agents. It sits inside the agent's reasoning loop, checks every turn against the task, the team's standard, and the code itself, and hands the fix back to the agent before anything reaches Git.

Every decision lands in a knowledge base the next session builds on, so nobody maintains instruction files across repos by hand.

The check also stands apart from the agent that wrote the code. Verity combines AI analysis, SAST, intent checks, formal proofs, and runtime analysis, so the result doesn't depend on the agent grading its own work against its own context.

From there, the split is simple: fix in the agent loop, enforce at the PR. Verity works alongside the agent, and the Codacy platform determines what ships at the pull request level and across the organization.

Treat context as part of your system

Coding agents now work across sessions, repositories, and teams, so the context they draw on is part of your engineering system. Your team owns the decisions and the reasons behind them. Tooling should keep that record current and enforce what's allowed to merge.

The principle to keep in mind is to give agents the right context so their work holds to your standards across every agent and repository, with fewer rounds of correction, and keep an independent check on the result, because an agent can't verify its work against the same material that shaped it.

Before deciding what to build first, check where your team sits on the AI Coding Maturity Scale.

AI context engineering FAQs

Is AI context engineering different from prompt engineering?

Yes, prompt engineering focuses on wording a single instruction well. Context engineering, on the other hand, covers everything an agent sees at each step, including files, standards, history, and check results, and it has to be kept current as a system.

Does a bigger context window solve context rot?

No, a bigger context window doesn't solve context rot. Chroma tested 18 models and found every one degraded as input length grew, even with the task held constant. A focused input of about 300 tokens beat the same facts buried in about 113,000 tokens of history, so curation matters more than capacity.

What is the difference between per-run and team-level context?

Per-run context is assembled automatically for a single task, such as the current diff or an open ticket. Team-level context, like architecture notes and coding standards, persists across tasks and engineers. It only stays useful if it's kept current, which at team scale means tooling that captures decisions as they happen.

How does AgentLinter relate to context engineering?

AgentLinter is an open-source scanner for AI agent configuration files such as CLAUDE.md and AGENTS.md, and Codacy runs it as part of its analysis. It flags exposed secrets, vague instructions, contradictions between config files, missing sections, and injection attack vectors.

AI-native SDLC: how to get there without rebuilding your development process in 2026

Codacy — Thu, 12 Nov 2026 09:53:46 +0000

Your team gave every developer a coding agent, and code now arrives faster than anyone can review it. However, the SDLC around the agent still runs at human speed, so the bottleneck moves to planning, review, and deployment.

Atlassian shows what changes when those stages speed up too, with issue cycle time falling from 11.5 days to 6.7 days.

An AI-native SDLC is a software development lifecycle in which automated verification keeps pace with how fast coding agents generate code.

In this article, we'll cover what changes at each stage of the SDLC and where human judgment should stay.

Key takeaways

An AI-native SDLC keeps Jira, GitHub, and CI/CD as the system of record and adds verification at the point of change.
Coding agents speed up one step, so the bottleneck moves to planning, review, and deployment, which still run at human speed.
Standards enforcement, security checks, and review break first: reviewers now verify work they didn't watch, at a volume no team can hire its way through.
Governance has to be executable. If a check is optional, the agent skips it, so hooks and merge gates replace advisory documents and sign-offs wherever a rule can decide.
Agents act up to the gate, and people decide at it, which keeps scarce senior review time on the changes that carry real risk.

Adding AI to individual steps is not the same as redesigning the SDLC flow

An engineering team can put a coding agent in every developer's terminal and still run an SDLC designed for a world where writing code was the slow part. The agent speeds up one step and leaves the rest of the flow untouched.

Most teams that adopt AI coding agents start exactly here: a faster way to produce a diff, dropped into the same intake process, review queue, approval gates, and sprint rituals that existed before the agent arrived.

When writing code collapses from weeks to hours, the steps whose delays used to go unnoticed become the visible constraint, because planning, review, and deployment still run at the pace of human meetings and human attention rather than the pace of agent output.

The bottleneck in the AI software development lifecycle relocates to those steps.

Reviewing a diff line by line made sense when a person had written every line, and a reviewer could reasonably assume the author understood the trade-offs behind each choice.

That assumption breaks down once an agent writes most of the change. The reviewer is now verifying work they didn't watch happen, at a volume no manual process was sized to absorb. This is what DORA calls the verification tax.

Agent output grows faster than any engineering organization can add reviewers, so no team can hire its way to that review volume. The way forward is to adapt the SDLC around the agent, so each stage produces work a machine can check, and people step in only where judgment is needed.

What an AI-native SDLC changes at each stage

The widely cited six-stage model from Anthropic's AI-native SDLC playbook gives engineering teams a shared vocabulary for this shift:

Plan: capturing intent so agents start from the right place

Planning in an AI-native SDLC means replacing drawn-out backlog rituals with a short, committed statement of intent that both a person and an agent can read and act on.

When that intent instead lives scattered across meetings, Slack threads, and half-written tickets, an agent starts from ambiguous inputs and generates volume in the wrong direction, which is often more expensive to unwind than no code at all.

This is the one stage where a verification layer should stay entirely out of the way.

Design: defining standards once so they apply everywhere

Design is where a team defines the standards a change must meet before any code gets written, so an agent working from the spec already knows the rules rather than discovering them in review.

When those standards live in a wiki page nobody rereads, or in one reviewer's memory, they get applied inconsistently from repo to repo, and that inconsistency compounds as code volume rises with agent adoption.

This is where a centralized policy engine earns its keep. It should let a team define coding standards and AI policies once and apply them consistently across all connected repositories and all agent-generated changes, rather than each team reinventing its own interpretation.

Build: checks inside the IDE and the agent's loop

During the Build stage, an agent explores a repository, drafts a plan, and edits multiple files in a fraction of the time that work used to take a person.

The problem is that if checks run only after the fact, an agent can produce a large diff with no feedback until it hits the review queue, and that queue backs up quickly once several agents are working in parallel.

Closing that loop requires quality and security checks that run in the IDE and in the agent's working session, so issues surface and get fixed while the agent is still working rather than after a human finally opens the pull request.

Test: coverage that keeps pace with generated code

Testing has to treat coverage as continuous rather than a separate phase tacked on at the end of a sprint.

When build velocity accelerates but test coverage doesn't keep pace, untested generated code quietly piles up, and the risk simply moves downstream to whoever discovers it in production.

Coverage tooling should tie coverage directly to the change itself, flagging untested lines in a diff before merge instead of leaving a team to discover them weeks later through an incident.

Deploy: quality gates and AI review at the pull request

At the Deploy stage, review and approval account for the largest chunk of human-speed time left in the pipeline.

CloudGeometry's analysis of the public Anthropic playbook makes a point worth raising: the real ceiling on how many agent sessions a team can run is the number of changes a person can properly review before quality erodes.

Independent quality gates should block a PR from merging when it introduces new quality or security issues, so the merge gate enforces consistently regardless of which reviewer is on call that week.

AI code reviewers, on the other hand, must flag what needs a closer look, so human attention goes toward judgment calls instead of mechanical checks.

Maintain: codebase-wide visibility over time

Maintaining an AI-native SDLC means keeping a live view of quality and security trends across every repository, as the codebase changes rapidly. Without that shared view, drift stays invisible until an incident forces someone to go looking for it.

At Atlassian, for example, agent loops in Jira now resolve 51% of potential vulnerabilities automatically.

A shared view should surface recurring issue classes and standards adherence across all connected repos rather than on a per-project basis.

Codacy as the verification layer across these stages

Codacy provides that verification layer across the stages where it fits, letting teams switch this layer on rather than building hooks, eval pipelines, and policy configuration from scratch:

Stage	How Codacy fits
Plan	Planning stays with your existing tools.
Design	Codacy Skills give coding agents the same coding standards and gate policies your team defines once at the organization level and applies to every repository.
Build	AI Guardrails runs quality and security checks in the IDE and inside the agent's session through the Codacy MCP Server. Verity reviews every agent turn through a stop hook: the Codacy Analysis CLI runs deterministic checks first, then an independent model reviews the session, with up to two self-fix attempts before it hands off to a person.
Test	Coverage tracks coverage against each diff, and coverage rules in quality gates flag untested changes before merge.
Deploy	Codacy's quality gates block merges that introduce new quality or security issues, while Codacy's AI Reviewer reviews each PR and flags what needs human attention.
Maintain	AI Risk Hub gives codebase-wide visibility into recurring issue classes and standards adherence across every connected repository.

Note: Everything here covers work up to the merge. Planning, release orchestration, and production monitoring stay with your existing tools.

From handoffs and approvals to artifacts and automated gates

The handoff itself changes shape in an AI-native SDLC: committed, machine-readable files that both agents and people can read replace meetings and status tickets as the way work moves from one stage to the next.

Each stage leaves behind a file the next stage can pick up, such as an intent note, a spec, a plan, or a diff with its test results.

What teams need are gates that replace sign-offs wherever a rule can decide, so a lint violation, a known vulnerability pattern, or an untested changed line gets the same deterministic check every time.

Where human judgment still belongs in the SDLC

Not everything in an AI-native SDLC should get automated away, and the line sits where context and accountability matter more than pattern-matching.

People still own intent, risk tradeoffs, and anything touching regulated data or business-critical logic, as no rule-based system can substitute for someone who understands why a decision matters to the business.

The operating model worth adopting is simple to state: agents act up to the gate, and people decide at it.

The verification layer validates everything it can first, and humans step in only where the system genuinely can't make the call, while remaining accountable for production decisions.

In other words, it means concentrating scarce senior review time on the handful of pull requests per week that touch something sensitive, and letting the gate clear everything else a rule already covers without waiting on a human in the loop.

What can teams change without rebuilding their SDLC?

A team doesn't need a platform rebuild to make its SDLC AI-native. It needs to keep its existing tools as the source of truth where auditors already expect them and start enforcement at the stages that hurt most once agents write the code.

Keep Jira, GitHub, and CI/CD as the system of record. Don't rip out tools auditors and other teams already depend on.
Link records to commits so the artifact trail stays intact and auditable from intent through merge.
Start with the verification layer at the stages that break first: review, security checks, and standards enforcement.

A team switches on enforcement at the point of change and expands coverage from there as confidence builds.

Why does governance have to become executable?

Instruction files and process documents describing how code should be written are advisory, and rules that are defined but not enforced don't survive contact with the volume of change agents now produce.

As we found running our own agents, if a check is optional, the agent skips it, because nothing forced the check to run.

Policy still matters: DORA's research shows organizations with clear AI acceptable-use policies see 451% higher AI adoption. Writing the policy down is where it starts; enforcing it is what makes these policies hold.

Hooks and merge gates make policy enforceable. The more manual gates a team keeps on the critical path, the more backlog and review overhead accumulates as agent output scales, which is precisely the governance tax a verification layer is built to remove.

Executable gates also produce the evidence AI code governance depends on. Once a meaningful share of new commits come from agents, a team needs a record of which tests and checks ran on each change, one of the basics of governing AI in software development.

FAQs around AI-native SDLC

What's the difference between AI-assisted development and an AI-native SDLC?

AI-assisted development describes a developer prompting an agent and reviewing the output in the same session. An AI-native SDLC goes further, redesigning the stages around the code (planning, standards, review, security, deployment) so they run at a pace that matches agent-generated volume instead of staying fixed at the old human-review cadence.

Do I need a platform team to run an AI-native SDLC?

No. The stages that break first once agents write most of the diff, namely standards enforcement, security checks, and review, can be covered by switching on a verification layer rather than building hooks, eval pipelines, and policy configuration internally.

Does Codacy replace Jira, GitHub, or my CI/CD pipeline?

No. Codacy sits alongside those tools as the verification layer covering standards, checks, and review up to the merge. Jira, GitHub, and CI/CD remain the systems of record for planning, version control, and release, exactly where auditors and other teams already expect to find them.

How is an AI-native SDLC different from just adding more AI coding tools?

Adding more coding agents accelerates the build phase without changing anything around it, so the gain gets absorbed by the same review queues and approval gates that existed before. An AI-native SDLC redesigns verification at the stages next to the build phase so the whole lifecycle moves faster, including the stages around code generation.

What October 2026 Taught Engineering Leaders About Preventing Supply Chain Attacks

Codacy — Thu, 12 Nov 2026 09:52:36 +0000

TL;DR

In one October week, five different layers of the software stack had critical problems at the same time.
Attackers went after the PHP flaws in large numbers, and change processes built for a normal week can struggle to keep up.
Much of the exposure came through dependencies and bundled components, so a useful first question is whether the vulnerable path is reachable in your code.
Being ready means an always-current inventory, checks in the pull request, pre-planned patch paths and credential hygiene, all funded as a running cost.

In one week of October, critical problems hit the PHP runtime, a compression library that HTTP clients depend on, a self-managed source control platform, a set of certificate authorities and a package on PyPI. A lot of that exposure came through components the affected teams didn't write themselves. Codacy's dependency scans found the eMudhra flaw in 576 repositories across 72 organizations.

One Week, Five Layers of the Stack Broke

The disclosures landed on 17 October (eMudhra) and 21 October (PHP2Shell, Brotli and GitLab). Here is what broke, layer by layer:

Runtime: PHP2Shell, CVE-2026-109618 and CVE-2026-109619, both rated critical (CVSS 9.2). CISA added both to its Known Exploited Vulnerabilities catalog on 26 October. Delft University's Threat Intelligence Lab tracked more than 50,000 exploitation requests from over 600 IP addresses. One researcher recorded successful shell execution on honeypots in a favorable setup, while Sophos revised its own report to scanning and failed attempts.
Library: an out-of-bounds write in Google's Brotli compression library, CVE-2026-111497, rated critical (9.2). Imperva showed it could be exploited for remote code execution against an official Node.js image. CISA added it to the Known Exploited Vulnerabilities catalog on 27 October. Akamai's researchers track it as Node2Shell.
Toolchain: remote code execution in GitLab Community and Enterprise Edition through bugs in Ruby's Onigmo regex engine. Our incident log rates CVE-2026-110264 at CVSS 10.0 with no exploitation reported, and CISA added a related GitLab flaw, CVE-2026-97229, which lets an unauthenticated user execute arbitrary code, to its KEV catalog on 29 October.
Trust layer: a key weakness at the eMudhra certificate authority, CVE-2026-111943, disclosed 17 October. It affected eight intermediate CA keys, which were revoked on 18 and 19 October.
Supply chain: a malicious LiteLLM release (v1.107.0), published to PyPI and Docker on 21 October, that carried credential-stealing malware.

Then the layers started stacking. CISA notes that a Linux kernel flaw, CVE-2026-46300, "can be chained with CVE-2026-109618 and CVE-2026-109619 to implant PHP webshells and run commands as root."

Any one of these would have been a normal bad week. The trouble was that every layer had its bad week at once.

The Patch Window Is Getting Shorter

Attackers did not wait for the patch cycle. AWS saw attempts to exploit the PHP flaws as early as 17 October, four days before public disclosure. The official clock was short too: CISA gave US federal agencies until 29 October, three days after the listing, to deal with them.

An attacker needs one exploit that works. A company needs a ticket, an owner, a change window and a test run before anything moves.

Change control exists for a good reason. Rushed patches can break production. So I'm not arguing for skipping the controls. I'd rather see teams decide the patch path before a disclosure lands, so a fast fix can still go through review, testing and a rollback plan.

The pressure is also getting worse. Verizon's 2026 Data Breach Investigations Report says software vulnerabilities have overtaken stolen passwords as the top way attackers get in, and that attack techniques "are now being bolstered by generative AI." If finding bugs gets cheaper for defenders and attackers alike, I'd plan for more critical CVEs a month, not fewer.

Much of Your Exposure Is Code You Didn't Write

Much of the risk in an incident like this sits in dependencies and container base images rather than in first-party code.

Codacy's own scan data shows how that played out.

Dependency scanning found the eMudhra flaw CVE-2026-111943 in 576 repositories across 72 organizations, with 640 findings still open (the CVE was assigned so that updated CA certificate bundles, such as ca-certificates and the copies shipped with Go, Java and Perl, could be tracked like any other package fix).

It found the Brotli flaw in 12 repositories across 11 organizations. Container scanning, which covers the Docker images customers upload to Codacy, found the eMudhra flaw in 96 repositories and the Brotli flaw in 47, with 163 Brotli findings still open.

The PHP2Shell flaws turned up in one repository's images. The GitLab flaw CVE-2026-110264 didn't show up in either scan, as you'd expect: it sits in a server teams host themselves, so it needs a patch path rather than a dependency fix.

So the question for the next morning is "is the vulnerable path reachable in our code?", and ideally you want the answer within hours, not days. Answering it completely is hard. A dependency map (see our guide to software dependency management) tells you whether the package is in your tree and how it got there. Software composition analysis narrows it down quickly, but that last check still needs someone looking at your code.

How to Prevent Supply Chain Attacks Before the Next Black October

Preparing doesn't mean another fire drill. It means changing the routine so the next disclosure is a short, rehearsed exercise rather than a scramble. Four habits are a good place to start:

An always-current dependency inventory. A software bill of materials generated once a year for an audit can be out of date the day a CVE lands.
Security checks in the pull request, before code merges. Catching a vulnerable dependency or an exposed secret at the PR is usually cheaper than finding it in production, and it is the part of CI/CD pipeline security developers actually see.
Pre-planned patch paths, including for self-hosted tools like source control. Decide now who patches what, in which window, and how to roll back. This is the answer to the change-control objection.
Credential hygiene. The LiteLLM release carried malware that steals credentials, so short-lived tokens, rotation and secrets scanning belong next to patching.

We cover the wider practices in software supply chain security.

Budget for Security as a Running Cost

Here is where Codacy fits. Dependency scanning shows the chain to a transitive vulnerability, with a prompt to find the affected functions in your code. That narrows the reachability question, although it is not full reachability analysis. Merge gates on SAST, secrets and dependencies run on every pull request, and you can export an SBOM in CycloneDX.

On the Business plan, every dependency in the codebase and every container image SBOM is re-scanned daily for new CVEs.

Whatever tools you use, the decision in front of a VP is about money and habits. Weeks like Black October are likely to come again. Fund the inventory, the PR checks and the patch paths as a standing budget line, the way you fund on-call. Otherwise you are likely to pay for them anyway, as emergency spend after the next big disclosure.

Supply Chain Attacks FAQs

What is a supply chain attack?

A supply chain attack is an attack that compromises software through something a team depends on, such as a library, a package registry, a build tool or a vendor, instead of attacking the team's own code directly. The malicious LiteLLM release on PyPI that carried credential-stealing malware is the example from this October week. For the full definition, see our guide to software supply chain security.

How to prevent supply chain attacks?

Preventing supply chain attacks means shrinking the time between a disclosure and your answer to "are we exposed?", because you cannot stop upstream code from breaking. Four habits help: an always-current dependency inventory, security checks in the pull request, patch paths decided before the disclosure lands, and credential hygiene such as short-lived tokens, rotation and secrets scanning. The aim is a short, rehearsed exercise rather than a scramble.

What is software composition analysis, and what is SCA in cyber security?

Software composition analysis (SCA) is the scanning of the open-source dependencies in a codebase, including transitive ones, to find known vulnerabilities in them. In practice it tells you whether a vulnerable package is in your tree and how it got there, which is the first half of the reachability question. Someone still has to check whether your code calls the affected functions, so SCA narrows it down quickly but doesn't finish the job. Our guide to software composition analysis goes deeper.

How to generate an SBOM?

Generating an SBOM means exporting a machine-readable inventory of every component in your software, ideally from a tool that scans your repositories automatically rather than from a manual spreadsheet. The format matters less than the timing: an SBOM produced once a year for an audit can be out of date the day a CVE lands. Codacy can export an SBOM in CycloneDX, and our SBOM guide covers the process.

What is a vulnerability remediation process?

A vulnerability remediation process is the agreed sequence a team follows from the moment a flaw is disclosed to the moment it is fixed: who owns it, which window it ships in, how it gets tested and how it rolls back. Those decisions are easier to make before a disclosure than during one. Change control exists because rushed patches can break production, so the goal is a fast fix that still goes through review, testing and a rollback plan.

The SOC 2 Evidence Checklist for Engineering Teams

Codacy — Thu, 12 Nov 2026 09:50:37 +0000

We built a SOC 2 evidence checklist because evidence is what we help engineering teams with every day. Proof that code is reviewed, scanned and fixed before it ships is one of the main reasons teams use Codacy. We know which questions land on engineering, and which ones are hard to answer. We also go through a SOC 2 audit ourselves every year.

This checklist covers the SOC 2 controls engineering usually handles in a standard SaaS company, from code review and CI to access, secrets, vulnerabilities, incident response and backups. For each one, you’ll see the evidence that proves it, what good looks like, how auditors test it and where teams usually fail.

Get started with the SOC 2 Evidence Checklist for Engineers

The interactive version, with all 75 checks, is on the SOC 2 Evidence Checklist page.

We wrote it with support from Secureleap, a compliance consultancy that helps companies prepare for SOC 2. They brought the insider knowledge: the evidence examples, sample sizes, common gaps and how most teams fail.

This checklist is written for a standard SaaS company: a cloud-hosted product, a version-controlled codebase and a conventional engineering workflow. It does not cover edge cases. It is provided as is, for guidance only, and does not guarantee a SOC 2 Report outcome. This checklist won’t replace your auditor or a security lead. But work through it before your first audit, or your next one, and you’ll find the gaps while you can still fix them. If in doubt, always confirm with your auditor or compliance advisor.

There are three ways to fill in the checklist: online on the checklist page, with the agent skill in your AI assistant, or in a spreadsheet with the full PDF guide beside it. The 75 questions are the same in each, so pick the one that fits how you work.

SOC 2 Compliance Checklist: Agent Skill

Available as a skill. Looks for answers in your connected apps.

👉 Download the SOC 2 checklist skill (.zip)

You can also download the skill version and run the checklist inside your AI assistant. The skill works in Claude, Claude Code and ChatGPT (where skills are enabled) and asks the 75 questions one at a time.

Context with every question. Each one comes with the detail from the full guide: the evidence that counts, what good looks like and how auditors test it.
It looks for the evidence first. It searches the tools your assistant is already connected to, such as Linear, GitHub or Jira, and shows you what it found and what it didn't. It only reads, and you confirm every answer.
A gap list at the end. Core gaps first, each with what to put in place to close it.

Your answers stay in the conversation and are not sent to Codacy.

SOC 2 Compliance Checklist: PDF and sheet

Available as spreadsheet. Plus, a comprehensive SOC 2 guide for engineering teams

👉 Download the spreadsheet and PDF guide (.zip)

Prefer a file you can share with the team? Download the checklist as a spreadsheet, together with the full engineering guide as a PDF.

The spreadsheet is a plain tracker: one row per check, with its tier and criteria. Pick Yes, Partly, No or Doesn't apply, add an owner and notes, and the answered and gap counts update as you go. It opens in Excel, Numbers or Google Sheets.
The PDF guide is the long read. If this is your first SOC 2 audit, start here: it explains every control in full, with evidence examples, what good looks like, what auditors test and who usually owns it. Each chapter is bookmarked, so you can jump to any topic from your PDF reader's sidebar.

What's in a SOC 2 compliance checklist for engineering teams?

Our SOC 2 compliance checklist covers 75 items organized under 18 topics that engineers will understand. Each item is a control engineering is expected to run, mapped to the SOC 2 criteria it supports. Items are tiered: 43 Core, 14 that apply only if you use something (CI/CD, containers, contractors, AI coding tools), and 18 Optional improvements (or "nice to haves"), where a "No" is not an audit gap.

Topic	Criteria	What's checked
Planning secure features	CC8.1	Security design review
Code review and approval	CC8.1	Protected branches, review before merge, named and traceable accounts, a ticket for every change, a second approval on every path
Automated checks before merge	CC8.1, CC7.1	Required checks, testing before production, code scanning (SAST), secret scanning, infrastructure code scanning
Coding rules	CC8.1	Secure coding standard
Emergency changes and bypasses	CC8.1	Emergency changes, restricted and reviewed bypass, production data fixes
Changes to the checks themselves	CC8.1	Pipeline files under review, pipeline admin access, pinned third-party actions
Environments and deployment	CC8.1, CC6.1	Separate production, deploys only through the pipeline, traceable build artifacts, a complete list of changes
Access and secrets	CC6.1, CC6.2, CC6.3	Production repositories, MFA, least privilege, approved grants, periodic access reviews, timely removal, SSO, just-in-time and break-glass access, secrets in a managed store, leaked secret rotation, per-service secret access, machine credential inventory
Vulnerabilities and dependencies	CC7.1, CC6.8	Dependency scanning, fix deadlines by severity, vulnerability exceptions, container and server scanning, DAST, penetration test and its findings
Infrastructure as code	CC7.1, CC8.1	System inventory, IaC through the pipeline, hardening baselines, configuration monitoring, drift detection
Test data	CC8.1, CC6.1	No production data in development or test
Contractors	CC6.2, CC6.3, CC9.2, CC1.4	Contractors in your access controls, contractor firms assessed
Secure coding training	CC1.4	Security training for engineers
Checking your controls still work	CC4.1, CC4.2	Controls checked between audits
Exceptions and policy review	CC5.3	Engineering policies owned and reviewed, exceptions to engineering rules
AI-assisted software development	CC8.1, CC6.1	AI changes follow the normal path, AI tools have scoped access, rules for what goes into AI tools
Security monitoring and incident response	CC7.2 to CC7.5	Central logs, alert rules with owners, triage records, tamper-proof logs, alerts when monitoring stops, threat detection, on-call, incident records, postmortems, corrective actions, exercises
Backups and encryption	CC9.1, CC6.1, A1*	Automated backups, restore tests, disaster recovery plan, isolated and immutable backups, encryption at rest and in transit, key management

*A1 applies only if Availability is in scope.

How often do SOC 2 audits find problems?

Most of the time. Few audits end in a qualified opinion, the auditor's verdict that one area of your controls doesn't meet the SOC 2 criteria: 10.9% of SOC reports (CBIZ, 2024). But more than half find exceptions (54.9% of SOC reports, CBIZ 2024): controls the auditor tested that didn't work as you described them, each one listed in the report your customers read. In 75 SOC 2 audits run by Lazarus Alliance between January 2025 and June 2026, the controls that broke most often were the ones engineering runs, and first-time audits had the most exceptions.

Finding	Share of audits
First-time SOC 2 audits with at least one exception	84%
Renewal audits with at least one exception	60%
Audits with access control exceptions	51%
Audits with change management exceptions	41%
Audits where the auditor asked for more evidence (most often access reviews, change tickets and log samples)	88%

Source: Lazarus Alliance, 2026 SOC 2 Audit Benchmark (75 audits).

What evidence do SOC 2 auditors ask engineering for?

System-generated records from the period, mostly. Settings exports, lists of merged pull requests, access review records, scan results, ticket histories, incident records. Then auditors sample from them. They usually come back for more, and an audit averaged about 1,450 pieces of evidence (Lazarus Alliance, 2026).

Here is one item from the checklist in full, so you can see the depth.

Review before merge Core · CC5.1 · CC8.1 · Usually owned by the engineering manager · Policy: Change Management Procedure The check. Every pull request merged to a protected branch has at least one approval from a person other than the author, given on the final version of the code. What good looks like. - The rule requires at least one approving review. - Stale approvals are dismissed when new commits are pushed, or approval of the most recent push is required. - Authors cannot approve their own pull requests, and bot or shared accounts do not count as approvers. - In a sample of merged pull requests, 100% show a human, non-author approval of the final commit before merge. Evidence examples. - Ruleset settings (required approvals, dismissal of stale approvals, approval of the most recent push). - System-generated list of merged pull requests for the period with author, approver, approval time, and merge time. - Sampled pull request pages. What auditors do. Auditors sample merged pull requests across the whole period, commonly 25 to 40, spread across repositories and months, and check for a non-author approval of the last commit before merge. Common gap. Approval was given before the last commits were pushed because stale approvals were not dismissed, or an admin merged without approval.

How to use the SOC 2 Evidence Checklist

Run the readiness check. Go through the items one at a time, or see them all in a table. Answer Yes, Partly or No for each.
Review your gaps. Core gaps come first. Optional items are listed separately because a No there is not an audit gap.
Track the work in the spreadsheet. Export your answers (the file opens in Excel, Numbers or Google Sheets), then assign owners, record where evidence lives and when it was last collected.
Prefer your AI assistant? Download the skill and run the checklist there, with your answers carried over.
Share the PDF guide. It explains every control in full for the engineers who will own them.

Where do engineering teams usually fail SOC 2?

In the same few places, audit after audit:

Evidence that can't be recreated. Last year's settings, last quarter's access review, the state of a branch rule in March. If nobody saved it at the time, it's gone.
Controls a person has to perform. Access reviews, incident timelines, vendor reviews. Automated controls run themselves; these depend on someone remembering, and leaving a record.
Rule exceptions. A vulnerability with no fix available, a hotfix that bypassed review. Exceptions to your own rules are allowed, but an undocumented one becomes an audit exception.
Undated screenshots. A screenshot without a visible date doesn't show when the control was in place.
Admin bypasses. An admin merge without approval, or a branch rule that admins can skip, breaks an otherwise clean change management sample.

The checklist's readiness check asks one question per item: can you show this evidence today? Your answers sort into gaps to fix first and optional improvements.

SOC 2 FAQ for engineering teams

What is SOC 2, and what does it mean for engineering?

SOC 2 is an audit framework from the AICPA. An independent CPA firm checks how you protect customer data and issues a report your customers can read. Customers ask for it in security reviews, often before they sign. In one survey, 61% of companies said compliance was required to win or renew contracts (Secureframe, 2026).

For engineering, SOC 2 comes down to one question: can you prove that the way you build and run software is controlled? That means showing that changes are reviewed, access is limited and removed on time, vulnerabilities are fixed within your deadlines, incidents are recorded, and backups can be restored. HR, legal and leadership own other parts. Engineering usually owns the largest share of the evidence.

Is SOC 2 a certification?

No. SOC 2 is an attestation. You don't get certified or pass; your auditor issues a report with their opinion on your controls, and lists any exceptions they found: controls that didn't work as you described them when tested. No tool or checklist makes you compliant. Only your auditor decides whether a control meets the criteria. If the problems in one area are serious enough that a criterion isn't met, you get a qualified opinion: a report that says your controls meet the criteria except in that area. It's the closest thing to failing, and it happened in 10.9% of SOC reports (CBIZ, 2024).

The Common Criteria engineering meets most often:

CC6, logical access: who can reach code, cloud, CI/CD and production, and how access is granted, reviewed and removed.
CC7, system operations: vulnerability scanning, monitoring, alerting and incident response.
CC8, change management: how code gets from a pull request to production.
CC4, monitoring activities: checking that your controls still work between audits.
CC9.1 and A1 (if Availability is in scope): backups, restore tests and disaster recovery.

SOC 2 doesn't prescribe tools, numbers or frequencies. It doesn't say "two approvals" or "patch criticals in 7 days". Your policies set those, and the auditor tests you against your own policies. That's why most of the work is in proving you follow them.

SOC 2 Type 1 vs Type 2: which do you need?

Type 1 checks that a control is designed correctly on a single date. Type 2 checks that it worked for the whole audit period, usually 3 to 12 months.

Most customers ask for Type 2. The difference matters most to engineering. A branch protection setting you screenshot today proves nothing about last March. You can't recreate last year's access review after the fact. Evidence has to be collected during the period, and screenshots need a visible date, or the auditor rejects them.

How long does SOC 2 take?

Typical ranges for a first audit:

Phase	Type 1	Type 2
Preparation	1 to 3 months	1 to 3 months
Observation period	None	3 to 12 months
Auditor fieldwork	2 to 5 weeks	2 to 5 weeks
Report	2 to 6 weeks	2 to 6 weeks

After the first report, most companies move to a rolling 12-month Type 2 period.

Here's what the cycle looks like at Codacy. We book our auditor in October or November, run a pre-check in December and go through the audit in January. The report comes later; last year it arrived in April. Between audits, some checks recur weekly and others every 20 or 30 days. Plan the evidence calendar before the audit calendar.

Does SOC 2 require specific tools?

No. SOC 2 doesn't name tools, and the tools in our checklist are examples. GitHub or GitLab, AWS or GCP, any scanner: what matters is that the control runs and leaves a record. The same goes for numbers. Where the checklist says "commonly 25 to 40" or gives fix deadlines, those are common choices, not requirements. Your policies set them.

Do you still need a checklist if you use Vanta or Drata?

Compliance platforms organize your program, track policies and pull some evidence from connected systems. They don't decide how your team reviews code, handles an emergency fix or documents a vulnerability exception. That's engineering work, and it's where audits find gaps.

Many teams use both: the platform for the program, the checklist to go item by item through the engineering side and see what's covered and what isn't.

Thank you to Secureleap, the compliance consultancy that prepared the expert content in this checklist with Codacy: the control statements, what good looks like, the evidence examples, how auditors test and the common gaps. SOC 2 doesn't prescribe tools, numbers or frequencies: where this checklist gives them, they are common choices, not requirements. This checklist doesn't make anyone compliant, and neither does any tool. SOC 2 is an attestation: your auditor issues a report, and only your auditor decides whether a control meets the criteria.

Review of Ruby Static Analysis Tools

Codacy — Wed, 11 Nov 2026 15:02:09 +0000

Having trouble deciding which tool to use for Ruby static analysis, or perhaps unsure whether you found the most common ones? Here's a small list that may be helpful.

This review was first written in 2015 and was updated in October 2026. The original three tools are all still actively maintained, but they have changed quite a bit, and a few newer tools (Standard, bundler-audit, and the Sorbet and Steep type checkers) have earned a place on the list.

1: RuboCop

Right out of the box, RuboCop enforces many of the guidelines outlined in the community Ruby Style Guide and, not only does it find problems for you, it can fix many of them automatically. It works both as a linter and as a code formatter.

The checks RuboCop performs are called cops, and they are grouped into departments. Core RuboCop ships more than 600 cops in departments such as Style, Layout, Lint, Metrics, Naming and Security, plus Bundler and Gemspec cops for your dependency files.

Rails cops are no longer part of the main gem: they were extracted into rubocop-rails, and performance cops into rubocop-performance. There are also official plugins for RSpec, Minitest and more, and you can write your own cops. Since RuboCop 1.72, the recommended way to load extensions is the plugins key:

# Gemfile
group :development do
  gem "rubocop", require: false
  gem "rubocop-rails", require: false
  gem "rubocop-performance", require: false
end

# .rubocop.yml
plugins:
  - rubocop-rails
  - rubocop-performance

Cops detect offenses. With the following sample code:

# frozen_string_literal: true

def camelMethod
   puts "This line has 3 spaces indentation"
end

RuboCop's offense report looks like this:

Inspecting 1 file
C

Offenses:

code.rb:3:5: C: Naming/MethodName: Use snake_case for method names.
def camelMethod
    ^^^^^^^^^^^
code.rb:4:1: C: [Correctable] Layout/IndentationWidth: Use 2 (not 3) spaces for indentation.
   puts "This line has 3 spaces indentation"
^^^
code.rb:4:9: C: [Correctable] Style/StringLiterals: Prefer single-quoted strings when you don't need string interpolation or special symbols.
   puts "This line has 3 spaces indentation"
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

1 file inspected, 3 offenses detected, 2 offenses autocorrectable

(The magic comment on the first line is there because, by default, RuboCop also flags files that don't have it.)

Autocorrection comes in two strengths: rubocop -a applies only safe corrections, while rubocop -A also applies unsafe ones that may change behavior. On an existing codebase, rubocop --auto-gen-config writes a .rubocop_todo.yml that disables current offenses, so you can start from a clean run and fix things gradually.

You can enable or disable specific cops and alter their behaviour, including their severity. There are six severity levels: info, refactor, convention, warning, error and fatal.

There's also a cache mechanism, enabled by default, so files that haven't changed don't need to be inspected again on large projects.

RuboCop integrates with editors through its built-in language server (rubocop --lsp, available since 1.53), and there is an official VS Code extension.

2: Standard

Standard Ruby brings the approach of StandardJS to Ruby. It's a linter and formatter built on RuboCop that ships a fixed, "unconfigurable" configuration for RuboCop's built-in cops and those in rubocop-performance. You can't change the rules, but you can still ignore files or individual lines, set the oldest Ruby version you support, and add plugins such as standard-rails.

# Gemfile
gem "standard"

bundle exec standardrb        # report offenses
bundle exec standardrb --fix  # apply safe fixes

# .standard.yml (optional)
plugins:
  - standard-rails

Standard also includes a language server (standardrb --lsp) and a Ruby LSP add-on for editor integration.

3: Reek

Reek detects code smells. Bad smells are symptoms that may (but not necessarily do) indicate a deeper design problem.

Reek includes checks for Control Couple, Data Clump, Feature Envy, Large Class, Long Parameter List, Nested Iterators, Simulated Polymorphism, Too Many Statements, Uncommunicative Name, Unused Parameters, Utility Function and more (see Code Smells for the full list).

Given this file:

# Builds greetings for a user.
class Greeter
  def initialize(name)
    @name = name
  end

  def sayHello
    "Hello, #{@name}"
  end
end

Here's Reek's output:

$ reek --no-documentation greeter.rb
Inspecting 1 file(s):
S

greeter.rb -- 1 warning:
  [7]:UncommunicativeMethodName: Greeter#sayHello has the name 'sayHello'

Without --no-documentation, each warning also links to the documentation for that smell.

The configuration is based on a .reek.yml file in the project directory (running reek --todo lib/ once generates one that silences all existing warnings, so you can adopt Reek gradually). Specific smells can be suppressed for specific methods or classes with source code comments, and many smells only trigger once a threshold is reached.

It does make sense; if you had a bowl of cooked rice you wouldn't say the dish smelled bad because of one roasted grain, but if you had too many of those that would probably spark a smell alert. Similarly, an instance wouldn't smell, but you could set up a limit to how many would.

Every smell has some basic options that allow you to enable detection or exclude parts of the project, and configuration can be set per directory, which is handy in Rails apps where controllers and helpers deserve different rules.

4: Brakeman

I'm including Brakeman on the list even though it is a very different tool than the other ones here, but there's a reason for this.

Brakeman is a static analysis security scanner for Ruby on Rails applications; however, it runs on code, not on the website itself, meaning that you don't need to set up your web server to run the tool, and you can start using it from day one, just as you're writing your first lines of code. It supports applications from Rails 2.3 up to Rails 8.x.

This tool will catch many known vulnerabilities such as SQL Injection and Cross Site Scripting (those are three different links, BTW), but the list goes on.

Since it parses the code itself, Brakeman will not catch problems related to the database or the web server itself but, on the other hand, the tool is capable of finding problems before they become exploitable.

# Gemfile
group :development do
  gem "brakeman", require: false
end

bundle exec brakeman                   # run from the Rails application root
bundle exec brakeman -o brakeman.html  # write an HTML report
bundle exec brakeman -I                # interactively manage config/brakeman.ignore

Each warning gets a confidence level: high, medium or weak. Reports can be written as text, HTML, JSON, SARIF, JUnit, Markdown, CSV and several other formats, and by default Brakeman exits with a non-zero status when it finds warnings, which makes it easy to fail a CI build. Jenkins users can collect results with the Warnings Next Generation plugin. Since Rails 7.2, newly generated Rails applications include Brakeman and run it in their default GitHub CI workflow.

One thing to be aware of: Brakeman is distributed under the Brakeman Public Use License. Using it to analyze your own software is free, but commercial uses such as offering it as part of a paid product or service require a commercial license.

5: bundler-audit

Brakeman looks at your code; bundler-audit looks at your dependencies. It checks your Gemfile.lock against the Ruby Advisory Database for gem versions with known vulnerabilities, flags insecure gem sources (http:// and git://), and lets you ignore advisories you have already worked around.

# Gemfile
group :development do
  gem "bundler-audit", require: false
end

bundle exec bundle-audit check --update  # update the advisory database, then audit Gemfile.lock

Since Rails 8.1, new Rails applications include bundler-audit by default, with a bin/bundler-audit script and a config/bundler-audit.yml file for ignored advisories.

6: Sorbet

Sorbet is a fast, gradual type checker for Ruby, built at Stripe. You opt files in with a # typed: sigil, from false (syntax, constants and signatures only) through true (type errors) to strict (every method needs a signature), and add signatures over time. The getting started guide uses Tapioca to generate type definitions (RBI files) for your gems:

# Gemfile
gem "sorbet", group: :development
gem "sorbet-runtime"
gem "tapioca", require: false, group: [:development, :test]

bundle exec tapioca init  # set up the sorbet/ directory and generate RBI files
bundle exec srb tc        # type check the project

Signatures are written in Ruby itself, and sorbet-runtime can also check them while your code runs:

# typed: true
require "sorbet-runtime"

class Greeter
  extend T::Sig

  sig { params(name: String).void }
  def initialize(name)
    @name = name
  end

  sig { returns(String) }
  def say_hello
    "Hello, #{@name}"
  end
end

Sorbet's language server provides editor features such as autocompletion and go-to-definition. Note that, according to its FAQ, the static checker runs on macOS and Linux but not on Windows.

7: Steep

Steep takes a different approach to type checking. It uses RBS, the type signature language that ships with Ruby as a bundled gem, and types are declared in separate .rbs files, usually under sig/. Steep doesn't infer types from your Ruby code, so you declare them first:

# sig/greeter.rbs
class Greeter
  @name: String

  def initialize: (String name) -> void
  def say_hello: () -> String
end

gem install steep
steep init   # generate a Steepfile, then list your code and signature directories in it
steep check  # type check

Signatures for third-party gems can be installed with rbs collection, and editors can use Steep through its language server (steep langserver).

Which one should I use?

As with any other list of tools, it boils down to a matter of personal choice, although for some scenarios there are some clear winners. Most of these tools are complementary rather than alternatives to each other:

Style and formatting: RuboCop if you want full control over the rules; Standard if you'd rather not configure anything. New Rails applications (since Rails 7.2) already come with RuboCop and the rubocop-rails-omakase rule set.
Design problems: Reek, if you're looking for signs of potential problems in how your classes and methods are structured.
Security of your Rails code: Brakeman.
Vulnerable dependencies: bundler-audit.
Type checking: Sorbet if you want signatures written inline in Ruby (and optional runtime checks); Steep if you prefer RBS, Ruby's own signature format, kept in separate files.

All of them can run locally, in a pre-commit hook or in CI. Hosted code quality platforms can also run them centrally on every commit and pull request: Codacy, for example, runs RuboCop, Reek and Brakeman for Ruby and picks up existing .rubocop.yml and config/brakeman.yml files (its documentation notes that, due to licensing, it doesn't support the latest Brakeman version).

Conclusion

There are several Ruby static analysis tools, each of them with their own strengths (and many of them are complementary, not just alternatives to each other).

In fact, there are quite a few missing from this list, and some of them may be quite relevant to what you're doing.

I would also recommend checking out Flay (finds structurally duplicated code), Flog (scores how complex, or "tortured", your methods are), Rails Best Practices (code metrics for Rails apps, available as the rails_best_practices gem) and RubyCritic, which wraps Reek, Flay and Flog into a single quality report. All of them still receive updates.

If there's any other you think deserves a mention, please let me know in the comments.

Top 5 SonarQube Cloud (SonarCloud) Alternatives in 2026

Codacy — Fri, 06 Nov 2026 15:08:15 +0000

SonarQube Cloud (formerly SonarCloud) is excellent if you want software quality assurance as a service while avoiding maintenance technicalities. However, it might not provide you with the comprehensive security tools or the tailored controls you need to protect your software assets.

So, what’s the best alternative for SonarQube Cloud users or teams seeking a different solution for managing code quality and security? Here are the five top SonarQube Cloud alternatives to consider in 2026.

What SonarQube Cloud Does

SonarQube Cloud is Sonar's (SonarSource's) fully managed, cloud-based service for continuous code quality and security analysis, and the SaaS counterpart to the self-managed SonarQube Server. It was called SonarCloud until October 2024, when Sonar renamed its products: SonarCloud became SonarQube Cloud, SonarQube became SonarQube Server, and SonarLint became SonarQube for IDE. If you're searching for SonarCloud, it's the same product.

The platform offers these key features:

Robust static code analyzer: SonarQube Cloud identifies bugs, vulnerabilities, code smells, and exposed secrets across more than 40 languages and frameworks, including Java, Python, Go, JavaScript, TypeScript, C, C++, and C#, as well as IaC platforms such as Terraform, CloudFormation, and Kubernetes. Branch and pull request analysis are built in, whereas on self-managed SonarQube they require a commercial SonarQube Server edition; the free Community Build lacks them.
Smooth experience with multiple integrations: SonarQube Cloud works with GitHub, GitLab, Bitbucket Cloud, and Azure DevOps. It pairs with a free IDE extension, SonarQube for IDE (formerly SonarLint), that scans for issues as you code. SonarQube for IDE supports more than 20 languages and runs in VS Code, JetBrains IDEs such as IntelliJ IDEA and WebStorm, Visual Studio, and Eclipse, as well as AI-native editors like Cursor and Windsurf.
AI-assisted Coding: On the Team and Enterprise plans, the AI CodeFix feature uses large language models to suggest fixes for issues detected by select rules in your projects. Developers can review and apply the suggested fixes in SonarQube Cloud or, using connected mode, directly in their IDE. Sonar also offers AI Code Assurance for reviewing AI-generated code and a SonarQube MCP Server that brings its analysis into AI coding assistants.
CI/CD Analysis: Sonar offers scanners and integrations for the leading continuous integration (CI) systems used today, allowing you to perform analysis as part of your regular continuous integration (CI) process, aka build process. Documented integrations include GitHub Actions, Bitbucket Pipelines, Azure Pipelines, GitLab CI, Jenkins, CircleCI, Amazon CodeCatalyst, and Codemagic.

SonarQube Cloud gives users a big picture of their code quality status on the project overview and summary pages, which display the number and type of issues discovered, coverage, code duplication, and more. It analyzes your pull requests and “decorates” them with additional information (issues, suggestions, quality gate status, etc.). Additionally, the platform provides a Web API to access its functionalities from applications.

When evaluating alternatives to SonarQube Cloud, consider the following factors:

Static vs. Dynamic Analysis: We recommend you go for a tool that offers both static application security testing (SAST) and dynamic application security testing (DAST) capabilities. SAST analyzes code at rest, while DAST tests live applications, so having both is crucial for comprehensive security.
Number of integrations supported: Evaluate how well the tool integrates with your existing development and deployment processes. This includes CI/CD pipelines, source control platforms, IDEs, and container registries. Seamless integration can significantly enhance the security workflow.
Ease of use: Assess the user interface and overall usability of the platform. A user-friendly interface can reduce developers' learning curve and increase adoption rates.
Availability of AI-assisted coding tools: Check if the alternative includes AI-assisted coding features, as they significantly enhance developer productivity. AI tools can quickly spot vulnerabilities as code is written and suggest ways to resolve them.

What SonarQube Cloud Misses

So why might you look beyond SonarQube Cloud for code quality analysis? Here are some limitations and challenges associated with SonarQube Cloud:

Limited flexibility and customization options: You can build custom quality profiles and quality gates, but SonarQube Cloud doesn't support custom rules, and as a SaaS product it can't be hosted on your own infrastructure. If you need custom rules or full control over your data to meet specific security standards, you'd have to run self-managed SonarQube Server instead.
Insufficient documentation: Some users have reported that the documentation is inaccurate (in some parts) and lacks essential information. Specifically, on the web API docs, one common complaint from the SonarCloud era is that parameters for some endpoints are missing or marked as optional when they’re not, and required parameters are completely missing.
Incomplete application security: SonarQube Cloud doesn't offer DAST, and SCA is only available through the separately sold SonarQube Advanced Security add-on.

Now that you know SonarQube Cloud's strengths and weaknesses, what alternatives should you explore? Let’s take a closer look.

Codacy

Codacy is a comprehensive alternative to SonarQube Cloud and is suited to individuals and organizations of all sizes. It offers security tools that address code quality and security issues without complicating the development process, alongside integrations that further streamline the code security process.

Key features:

All-in-one platform: While many other solutions focus on one area, such as security, Codacy takes a more holistic approach to application security by packaging quality, security, coverage, and AI governance features in one solution. Codacy unifies various vulnerability detection tools in one platform, making it ideal for teams that want to handle code quality and security in one place.
Developer-first design: Codacy is designed with simplicity in mind. The platform integrates with GitHub, GitLab, and Bitbucket, connects to Jira and Slack, and runs well-known open-source analyzers such as Trivy, Checkov, and Opengrep. Codacy’s IDE extension lets developers scan for vulnerabilities as they write code in VS Code, IntelliJ, Cursor, and Windsurf. There’s also a CLI tool for local scans, and the platform has a Security and Risk Management dashboard.
DevSecOps in a box: Codacy's wide array of tools enables teams to integrate security testing at every stage of the software development process. Its offerings include SAST, SCA, hard-coded secret detection, and infrastructure-as-code (IaC) analysis, with DAST and container image scanning available on the Business plan and penetration testing available at an additional cost. This gives product owners a 360-degree view of their application security status and risks.
AI-assisted coding: Artificial intelligence (AI) has undoubtedly become an integral part of software engineering, and Codacy helps you keep AI-generated code safe. Its AI features build on its static analysis tools: Codacy Guardrails checks code written by AI coding agents in the IDE through an MCP server, and the AI Reviewer reviews pull requests with suggested fixes (currently for GitHub repositories on the Team and Business plans).

Beyond these, Codacy offers OWASP Top 10 coverage, test coverage reporting with merge gates (coverage shows engineering teams how much code is being tested and can require them to maintain minimum coverage levels automatically), and analysis across more than 40 languages.

Codacy’s Security and Risk Management dashboard presents an organization-wide view of the security health of your repositories, while repository and organization dashboards track coverage and give a detailed report on issues found, such as issues concerning code style, best practices, and security.

Codacy automates code reviews by analyzing your codebase and pull requests against a set of customizable coding standards, best practices, and quality gates. It’s also designed to work out of the box, requiring minimal configuration.

Codacy's more recent additions focus on AI governance. AI Inventory discovers the AI models, SDKs, and coding tools used across your repositories, and the AI Risk Hub summarizes AI-related risk and policy compliance at the organization level (both are Business plan options).

Why Choose Codacy Over SonarQube Cloud

While both tools excel at scrutinizing your code for bugs, code smells, and duplications, Codacy covers more of the application security stack in one product: dependency scanning (SCA) is part of its standard scanning, and DAST and container scanning are available on the Business plan, whereas SonarQube Cloud doesn't offer DAST and sells SCA as an add-on. Codacy also lets teams tailor analysis to each project or across the organization, using coding standards, tool configuration files, and repository settings.

Codacy's coverage tracking and merge gates enable teams to monitor, maintain, and improve test coverage. And because it runs widely used open-source analyzers, teams can often reuse the linter configurations they already have.

Snyk

Snyk is another solid alternative to SonarQube Cloud that might be a great fit for teams whose needs are centered around code security. While it doesn't cover code quality the way Codacy does, the platform offers quality vulnerability detection features and integrates with many developer tools and workflows.

Key features:

Comprehensive security coverage: To keep vulnerabilities away from your codebase, Snyk offers six key products: Snyk Code (secures your code as it’s written), Snyk Open Source (helps you avoid vulnerable dependencies), Snyk Container (helps keep your base images secure), Snyk IaC (helps fix IaC misconfigurations in-code), Snyk API & Web (finds and tests APIs and web apps), and Snyk Secrets (stops hardcoded secrets before commit).
Developer-friendly experience: Snyk offers integrations with IDEs, Git repositories, CI/CD pipelines, and container registries, a rich set of APIs, and many other developer-centric features. It also has a CLI tool that you can run locally to scan your projects for security issues, including security vulnerabilities and license issues.
Dedicated vulnerability database: Snyk says its Snyk Vulnerability Database covers three times more vulnerabilities than the next largest public database. It's hand-curated by Snyk's researchers, and Snyk's broader security intelligence combines public sources, developer community data, proprietary research, machine learning, and human-in-the-loop AI to adapt to evolving security threats.
AI-assisted coding: DeepCode AI, the engine Snyk built from its acquisition of DeepCode, drives Snyk's one-click security fixes (Snyk Agent Fix) and context-aware risk scoring, enabling developers to build quickly while maintaining security. Developers can easily review suggested fixes directly within their IDE or pull requests and adjust as needed.

Snyk can find and resolve vulnerabilities from IDEs, repositories, pipelines, and container registries. The platform offers customized security controls, automated fix recommendations, APIs, a detailed documentation/resource center, and a centralized user dashboard area. Its public Snyk Security Database lets anyone look up known vulnerabilities by package (the former Snyk Advisor package site now redirects there), and its newer Evo products focus on securing AI agents and AI-native applications.

Why Choose Snyk Over SonarQube Cloud

Snyk’s primary strength lies in its ability to scan and identify vulnerabilities within your code and dependencies. This emphasis on security is a great advantage for developers who want to protect their projects from potential risks.

However, SonarQube Cloud offers much more than security. It thoroughly examines your code for bugs, code smells, and duplications, providing a complete picture of its overall health. Another benefit of SonarQube Cloud is that it allows you to analyze individual branches in each repository.

So while Snyk is the stronger choice for security depth and SonarQube Cloud is the stronger choice for quality (with SCA only as an add-on and no DAST), Codacy covers both quality and security in one platform.

Veracode

Veracode is an application security platform, which it now describes as an application risk management platform, designed to secure different application types. As a SonarQube Cloud alternative, it follows the “secure-by-design” AppSec methodology and provides diverse security tools, including robust and accurate static application security testing (SAST). These features make Veracode a good option if you want safety and reliability.

Key features:

Comprehensive security coverage: Enterprises with complex security needs will benefit from Veracode’s extensive security coverage. The platform detects vulnerabilities and safeguards your application with SAST, DAST, SCA, container and IaC security, PTaaS (Penetration Testing as a Service), and Package Firewall, which blocks risky open-source packages.
Smooth Developer Experience: Veracode has many integrations available to users, including the major IDEs, Git providers, CI/CD systems, and issue trackers that many developers already use. Its IDE plugins cover Eclipse, JetBrains IDEs, Visual Studio, and Visual Studio Code; in VS Code, Veracode Scan runs SAST and SCA scans on your projects to find security vulnerabilities as you work.
Unified risk view: Veracode Risk Manager, its application security posture management (ASPM) product, provides unified visibility across findings so teams can prioritize and remediate what matters most.
AI-assisted flaw remediation: Veracode Fix uses AI to secure your code with suggested fixes. This feature is integrated into their IDE extensions, allowing developers to leverage AI for improved productivity and an overall smooth developer experience.
High-accuracy scanning: Veracode advertises a 1.1% false-positive rate, which helps teams focus on actual flaws and cuts the noise commonly seen in some other static code analyzers.

Veracode has user dashboards, hands-on Security Labs, and eLearning where developers can build secure coding skills, plus application security consulting for teams that need help remediating flaws. It works with most major programming languages and frameworks.

Overall, it's worth mentioning that Veracode only does security scans; it doesn't perform code quality scans.

Why Choose Veracode Over SonarQube Cloud

While SonarQube Cloud is praised for enforcing coding standards, its security coverage centers on static analysis (SAST, secrets, and IaC scanning), with SCA available only as an add-on and no DAST. That's why many do not regard it as a comprehensive developer security platform.

In contrast, Veracode provides a broader range of services, including SAST, DAST, SCA, penetration testing, and application security consulting. It is appealing as an all-in-one app security platform, especially for larger enterprises that seek to leverage its comprehensive services.

Veracode isn’t perfect, though. Beyond the lack of code quality analysis, some reviewers report that the platform can be slow to work with on large applications.

DeepSource

DeepSource is a robust alternative to SonarQube Cloud that now describes itself as an AI code review platform, combining static analysis with AI review agents and automated fixes. It's a good fit for developers looking for a dependable static analyzer with little setup.

Key features:

Comprehensive security tools: DeepSource provides a comprehensive suite of tools, including static analysis, vulnerability detection, infrastructure as code (IaC) analysis, secrets detection, open-source dependency scanning (SCA) with license compliance, and code coverage. Beyond scanning your source code, DeepSource analyzes your dependencies and configurations for potential security risks.
Developer-friendly experience: The DeepSource platform integrates with GitHub, GitLab, Bitbucket, and Azure DevOps, including self-managed versions such as GitHub Enterprise Server and Bitbucket Data Center. Developers can install its Visual Studio Code extension to catch issues early and resolve them without leaving their IDE window.
Low false positive focus: DeepSource says its analysis surfaces "high-signal, low false-positive issues" and reports the highest F1 score on the OpenSSF CVE Benchmark in its own published comparison.
AI-assisted code fixes: DeepSource's Autofix provides verified, pre-generated patches for most issues, and its AI review agent adds context-aware comments on pull requests.

DeepSource offers continuous monitoring and customizable security rules, helping teams uphold high security and compliance standards throughout the software development lifecycle. Its centralized dashboard allows users to visualize key metrics, track code health trends, and pinpoint areas for improvement. In addition, customers can connect DeepSource to their favorite data warehouse or business intelligence (BI) tools through DeepSource’s GraphQL API and webhooks.

Why Choose DeepSource Over SonarQube Cloud

Some SonarQube Cloud users find the results noisy, with many false positives. DeepSource, on the other hand, emphasizes low false-positive results and allows users to suppress the false positives that do appear.

Furthermore, DeepSource is relatively easy to set up and use, doesn't require CI configuration for its core analyzers, and integrates with GitHub, GitLab, Bitbucket, and Azure DevOps. Teams that need to keep code in-house can also run the self-hosted DeepSource Enterprise Server.

However, it's important to note that DeepSource's SCA and AI review are billed on usage beyond included allowances, and SCA isn't part of its free Open Source plan.

Checkmarx

Checkmarx One is an application security platform developed by Checkmarx. It offers both static and dynamic analysis capabilities, AI agents, actionable insights, integrations with popular tools, and a developer-focused experience, enabling its users to integrate security testing at all stages of their development process.

Key features:

Integrated security platform: Checkmarx One boasts an extensive set of security features for code security (SAST, secrets detection, IaC security, and API security), supply chain security (SCA, malicious package detection, container security, and SBOM reports), and runtime security (DAST). It’s a unified platform that integrates and automates multiple AppSec capabilities within your SDLC to simplify management and reduce TCO.
AI agents: The Checkmarx One Assist family includes Developer Assist, which catches issues in the IDE before code ships, and Triage Assist and Remediation Assist, which rank findings by attackability and propose fixes.
Seamless developer experience: Checkmarx One allows developers to incorporate tools they’re familiar with into the code security processes. The platform integrates with GitHub, GitLab, Azure DevOps, and Bitbucket, CI/CD tools, and bug ticketing systems. In addition, Checkmarx offers IDE extensions for Visual Studio Code, JetBrains IDEs, Visual Studio, and Eclipse, enabling developers to analyze their code for vulnerabilities as they write it.
Malicious package protection: Checkmarx has automated scanning technologies and a large proprietary database of malicious packages. Both help developers identify and remediate dangerous open-source code in their applications.

Beyond these, Checkmarx One supports a wide range of programming languages, personalized secure coding training via Codebashing, and compliance mapping to standards like the OWASP Top 10, PCI DSS, and NIST. It has a comprehensive resource center and a visual dashboard that displays the overall security status of your projects.

Checkmarx also offers ASPM, which gives AppSec teams the insights they need to focus on fixing what’s most important while letting developers get back to business.

Why Choose Checkmarx Over SonarQube Cloud

SonarQube Cloud's security features are relatively limited compared to Checkmarx. Small to medium-sized organizations may find SonarQube Cloud adequate for their needs, but larger ones will likely benefit more from Checkmarx due to its extensive capabilities.

While SonarQube Cloud's security features are narrower than Checkmarx’s, its static code analyzer is quite good. We recommend SonarQube Cloud if your application doesn’t require dynamic testing or complex features other than static code analysis.

But just like the other SonarQube Cloud alternatives Snyk and Veracode, Checkmarx focuses on application security rather than code quality, so teams that need both will need another tool, or a platform like Codacy that covers quality and security together.

Choose The Right Option For Your Team

Choosing the right quality assurance tool involves more than just evaluating features; it’s also essential to consider how intuitive the tool is. Codacy brings quality and security together in one platform and is designed to be easy to use, which is an important factor to consider if you intend to pick a tool you’ll stick with for the long run.

Of course, the choice is yours to make. Our goal with this article is to show you what each tool offers and the areas they’re deficient in.

Deciphering JavaScript Checkers: Know Why and When You Might Use Them

Codacy — Wed, 04 Nov 2026 15:03:40 +0000

We’re all familiar with rules of grammar, which aid in communication by letting us know when to use a comma, how to spell a word or the proper use of prepositions.

Tools such as Grammarly allow you to run text through a checker to ensure you’re following those rules and make suggestions to improve the writing overall.

There are also checkers out there to help with improving code quality, similar to the way Grammarly improves writing quality. In this article, we will go over JavaScript checkers. JavaScript and TypeScript together make up the largest language ecosystem on GitHub, and GitHub’s Octoverse 2025 report found that TypeScript became the most used language on GitHub in August 2025, measured by monthly contributors. JavaScript checkers, or linters, are great tools for making sure your entire team is writing high-quality code and following the same rules across the board.

Let’s take a look at what these tools are, why you want to use them, and how to best take advantage of them.

Static Code Analysis

When you have a large project, you need programmatic tools to validate the correctness and ensure code quality. The idea is to check the code and predict but also find actual issues in it without actually running or compiling the code.

With JavaScript code, the tools used to perform a static analysis fall into the categories of formatters and linters.

Formatters do pretty much as the name implies. They check your code for consistency in how it is formatted — like whether you used tabs or a specific number of spaces, what type of string delimiters are used, and even line widths.

A linter can handle formatting checks, but also scan source code for more complex issues. They use a set of rules to find any predefined issues to watch out for. Many of these rules are generic, but you can define specific rules for your project. In practice, most teams now pair a linter with a dedicated formatter: ESLint deprecated its own formatting rules in v8.53.0 (November 2023) and recommends using a source code formatter such as Prettier instead.

Linting tools can help in several aspects; besides flagging issues and providing warnings, they also provide suggestions for best practices consistent with the programming language being used. They not only flag style issues, programming errors and bugs, but also more serious issues, like security vulnerabilities.

Linters, also called static analyzers, perform static code analysis by providing alerts and finding the above errors and issues, before the code is compiled and actually runs. They help automate the code review process: they not only enable developers to find bugs much earlier and keep a consistent code style, but more importantly, they boost productivity; with linters in place, it helps save time in code reviews and you can spend more time developing products, or fixing other bugs.

JavaScript Checking Tools

A JavaScript checker is a kind of code review tool meant to automate the review process. Tools come in all types and for all languages. Codacy integrates several of them across the more than 40 languages it supports, including a few specifically for JavaScript and TypeScript. For JavaScript static analysis, it includes ESLint, Oxlint, Biome, PMD, and Opengrep. JavaScript checkers catch problems you might miss when trying to review lines and lines of code.

Here’s where the main JavaScript checkers stand as of October 2026:

ESLint is a pluggable linter in which every rule is a plugin. ESLint v10.0.0, released on 6 February 2026, completely removed the legacy eslintrc config system, so .eslintrc.* and .eslintignore files are no longer read. Configuration now lives in a “flat config” file such as eslint.config.js or eslint.config.mjs.
typescript-eslint is the tooling that lets ESLint support TypeScript. It adds TypeScript-specific rules, including type-aware rules that use the TypeScript compiler’s type information.
Biome is a formatter and linter in a single tool, written in Rust. Its formatter covers JavaScript, TypeScript, JSX, TSX, JSON, HTML, CSS, and GraphQL, and the project reports 97% compatibility with Prettier. Its linter has more than 500 rules drawn from ESLint, typescript-eslint, and other sources.
Oxlint is a Rust-based linter for JavaScript and TypeScript from the Oxc project, with more than 870 built-in rules. Its documentation suggests running it alongside ESLint during a migration, using eslint-plugin-oxlint to turn off the ESLint rules that overlap.
Prettier is an opinionated code formatter. It doesn’t look for bugs; it rewrites code into one consistent style. Prettier’s docs recommend installing eslint-config-prettier so ESLint and Prettier don’t conflict.
JSLint, Douglas Crockford’s original JavaScript code quality tool, is still maintained, with new editions released throughout 2026.
JSHint, which originated from the JSLint project in 2010, hasn’t had a release since version 2.13.6 in November 2022. Codacy deprecated JSHint in October 2023 because it had stopped being updated, and recommends ESLint instead.

For example, here’s the minimal flat config from the typescript-eslint getting started guide, which turns on the recommended rules from both ESLint and typescript-eslint. First, install the packages:

npm install --save-dev eslint @eslint/js typescript typescript-eslint

Then create an eslint.config.mjs file in the root of your project:

// @ts-check

import js from '@eslint/js';
import { defineConfig } from 'eslint/config';
import tseslint from 'typescript-eslint';

export default defineConfig({
  files: ['**/*.{js,ts}'],
  extends: [js.configs.recommended, tseslint.configs.recommended],
});

And run ESLint on your project:

npx eslint .

Potential Uses of JavaScript Checkers

JavaScript checkers provide several different functions. A specific tool might offer one or all of the following use cases.

Code Checking

Code checking entails scanning source code and looking for potential problems. This could be anything from breaking a naming convention or unused code to potential issues in performance and complexity.

The main goal is checking to see that your code follows the specific coding standards you have set up. Your team might follow a common convention such as Google’s style guide, or you might have your own in-house rules to guide you. A good tool should be fully configurable to follow whichever style you prefer.

Code uniformity means all your developers name the classes and properties the same way based on the standards you develop. The more people and the larger the project, the more essential it is to keep a handle on this.

Documentation Enforcement

Along with your code standards, inline documentation plays a key role in making sure everyone stays on the same page. This is especially true for developers joining a new team. Your checker can ensure that there are comments documenting your code so that it is easier for everyone to adhere to your standards. It is also important for adding and keeping track of TODOs and FIXMEs.

Bug Finding

JavaScript error checkers also look for general bugs in the code, comparing what you’ve written against common bug patterns. These can cover correctness, malicious code, performance, and security, among others. Security is an ongoing problem for JavaScript so a tool with additional checks for common vulnerabilities, such as types of injection and sensitive data exposure is important to have.

When Do You Check Code?

With a language like JavaScript, you actually have a choice as to when and how to check the code. Many developers run a static analysis on their own machine before sending off changes.

Your first time to do a code check might be in your own local environment before you submit the code to a shared repository such as GitHub, even though the checks done locally can be exactly the same as the ones done on the continuous integration server.

A later check might also be done manually by fellow developers during a regular code review process. The goal is to get the code as consistent, maintainable, and correct as possible before it hits the production server.

Deploy Your JavaScript Checkers

There are many tools used in code analysis, and we recommend you check out the array of JavaScript checkers if you care about code quality and consistent standards, and want to automate the code review process.

At Codacy, we integrate several JavaScript and TypeScript checkers, including ESLint, Oxlint, Biome, PMD, and Opengrep. Codacy analyzes commits and pull requests automatically, giving you details on any problems found, and for ESLint issues it can also suggest fixes. This also aids in tackling technical debt, which is a well-known term in software development. Automating the routine checks leaves human reviewers more time for design and logic, which matters for any team and overburdened developer, as the pressure to deliver well-built products and new releases with high quality fast and safely is more important than ever.

Whichever tools you choose, the key is consistency: pick one linter and one formatter, and run them with the same configuration on every developer’s machine and in CI.

Top 5 Veracode Alternatives in 2026

Codacy — Mon, 02 Nov 2026 15:18:07 +0000

Founded in 2006, Veracode is one of the oldest application security platforms in the industry. But like most other platforms, it has its fair share of flaws and limitations, which has prompted many customers (and perhaps you, too) to seek more suitable alternatives for your development team.

If you’re a Veracode user or part of a team considering an upgrade from Veracode, here are the top five Veracode alternatives to consider in 2026.

What Veracode Does

Every tool has strengths and weaknesses. Our goal is to show you the pros and cons of each tool, leaving you to decide which one best suits your needs.

Veracode, which now describes itself as an application risk management platform, offers these key features:

Comprehensive security coverage: Veracode offers Static Analysis (SAST), Software Composition Analysis (SCA), Dynamic Analysis (DAST), Penetration Testing as a Service (PTaaS), Container Security (including IaC and secrets checks), Package Firewall, External Attack Surface Management, and Veracode Risk Manager (ASPM, formerly Longbow). This makes it a solid option for enterprises seeking a unified security platform to build and secure their software from code to the cloud.
Frictionless Developer Experience: Veracode brings security to developers with over 40 integrations into IDEs, SCM tools, CI/CD tools, and ticketing systems. Its Veracode Scan IDE plugins for Visual Studio Code, JetBrains IDEs, Visual Studio, and Eclipse combine SAST and SCA scans in a single plugin. Veracode says its static analysis supports more than 100 languages and frameworks.
Comprehensive vulnerability database: Veracode has a dedicated vulnerability database that catalogs open-source libraries, their versions and licenses, and associated vulnerabilities, including public CVEs and Veracode’s own research.
AI-assisted flaw remediation: Veracode Fix suggests AI-generated fixes for flaws found by static analysis. This feature is available in Veracode’s IDE plugins and as a command-line tool, and Fix for SCA findings reached limited availability in September 2026.
Accurate analysis and low false positives: Veracode reports a false-positive rate of less than 1.1% out of the box for its static analysis.

In addition to these benefits, Veracode offers multi-language support, e-learning and Security Labs training, intuitive user dashboards, and Best Next Actions in Veracode Risk Manager (to remove the most risk with the least amount of effort). When evaluating alternatives to Veracode, consider the following factors:

Comprehensive security coverage: Assess whether the alternative offers robust and wide-ranging security testing capabilities, including static and dynamic analysis and support for multiple programming languages and frameworks.
Integration and workflow compatibility: Evaluate how well the tool integrates with your existing development and CI/CD workflows. Seamless integration can significantly enhance productivity and ensure that security testing becomes a natural part of your development process.
Scalability and performance: Consider the tool’s ability to scale with your organization’s needs and its performance impact on your development environment. It should handle growing codebases and increasing security demands efficiently without slowing development.
Cost and licensing: Review the pricing structure and licensing options to ensure they align with your budget and provide good value for the features offered. Some tools may offer more flexible or cost-effective plans based on your specific requirements.

What Veracode Misses

What are some reasons you might explore alternatives to Veracode? Here are a few limitations and challenges linked to Veracode:

No code quality analysis: Veracode focuses on security. It doesn’t analyze maintainability, complexity, duplication, or test coverage, so teams that care about code quality need a separate tool.
Cloud-only deployment: Veracode is fully SaaS and doesn’t offer an on-premises installation, which can be a hurdle for teams with strict data-residency or air-gapped requirements.
Build requirements for some languages: Static analysis of .NET, C/C++, and Ruby still requires compiled artifacts, which adds a packaging step. Veracode accepts source code for languages like JavaScript, TypeScript, Python, and Go, and added build-free Java source scanning in 2026.
Expiration for risk acceptance: Veracode’s mitigation workflow doesn’t let you set an expiration date when you accept the risk of a flaw, so accepted risks stay accepted until someone revisits them. This requires manual tracking, which can be cumbersome.

Now that you know Veracode's good and bad sides, what alternatives should you consider? Let’s take a look.

1. Codacy

Codacy is our own product, so it’s first on our list. It’s a developer-friendly alternative to Veracode that brings code analysis, security, and test coverage tools together in one platform, including checks for AI-generated code.

Key features:

Integrated AppSec platform: Codacy combines code quality analysis (error-prone code, complexity, duplication, and code style), security scanning, and test coverage tracking throughout the SDLC, helping teams raise their security standards without complicating their workflow.
Developer-first design: Codacy adapts to your code review process with integrations for GitHub, GitLab, and Bitbucket, Jira for issue sync, Slack for alerts, and open-source code analysis tools like Opengrep, Trivy, ESLint, PMD, and SonarC#. It also provides a CLI for local scans, a free IDE extension for VS Code, Cursor, Windsurf, and JetBrains IDEs, and organization-wide dashboards.
Security-focused approach: The platform covers SAST, SCA (including malicious package detection), hard-coded secret detection, and infrastructure-as-code (IaC) scanning. On the Business plan, it adds DAST (powered by ZAP) and container image scanning, and manual penetration testing is available as a separately billed service. Findings from these scans come together in a security and risk management overview.
AI-assisted reviews and fixes: Codacy’s AI Reviewer adds context-aware feedback and fix suggestions to pull requests on GitHub (Team and Business plans), with AI-enhanced comments available on GitLab and Bitbucket. In the IDE, Codacy Guardrails uses the Codacy MCP Server and CLI to scan AI-generated code so coding agents can fix issues before they’re committed.

Codacy comes with pre-configured coding standards and rulesets based on best practices, making it easier for developers to use the tool without doing much configuration. Its UI dashboards offer insights into your code’s health and configuration options, like quality gates, security rules, integrations, and other settings.

Codacy automates code reviews, and monitors code quality on every commit and pull request in over 40 languages (including infrastructure-as-code and configuration formats). It provides detailed reports on issues found, such as issues concerning code style, best practices, and security.

Codacy runs as a cloud service connected to GitHub, GitLab, and Bitbucket Cloud; Codacy Self-hosted is no longer available to new customers. Recent additions include the AI Risk Hub and AI Inventory (Business plan), which track AI usage, AI coding policies, and AI-related risks across repositories, and Verity, a reviewer for AI coding agents that’s currently in public beta.

Why Choose Codacy Over Veracode

While both offer a wide array of security features, Codacy is designed to start analyzing repositories with pre-configured standards and no CI/CD pipeline integration, which makes it quick to set up.

Codacy allows teams to set custom quality and code style thresholds, ensuring the code analysis process is tailored to meet the project's specific requirements. Veracode, on the other hand, focuses on security and doesn’t offer code quality analysis. Veracode does offer things Codacy doesn’t, such as binary static analysis, a package firewall, and external attack surface management.

Codacy's emphasis on code quality metrics and its ability to detect issues early in development makes it a valuable tool for improving code standards and reducing technical debt.

2. Snyk

Unlike Codacy, which offers a more comprehensive alternative to Veracode, Snyk specializes in security. Snyk can take care of an organization's code security needs by itself. However, Snyk has retired its code quality findings, so teams looking to transition from Veracode must pair it with a separate code quality solution.

Key features:

Comprehensive security coverage: Snyk offers six core products to take care of your application security needs:

Snyk Code secures your code as it’s written
Snyk Open Source helps you avoid vulnerable dependencies
Snyk Container helps keep your base images secure
Snyk IaC helps fix IaC misconfigurations in-code
Snyk API & Web (DAST) finds and tests APIs and web apps
Snyk Secrets stops hardcoded secrets before commit

Developer-first design: Snyk integrates with more than 100 tools, including popular IDEs, SCM tools, CI/CD tools, container registries, AI coding assistants, and notification and ticketing systems. Snyk weaves security expertise into your IDE, repositories, and workflows through these integrations.
Dedicated vulnerability database: Snyk’s vulnerability database contains a registry of open-source vulnerabilities and cloud misconfigurations. The database is maintained by Snyk’s dedicated security research team.
AI-assisted coding: Snyk’s DeepCode AI engine powers Snyk Agent Fix (formerly DeepCode AI Fix), which suggests security fixes so developers can build fast while staying secure. Developers can quickly review the suggested fix—provided in line with their code right from the IDE—and adjust if needed.

Beyond these, Snyk supports 19+ languages and provides a rich set of APIs for third-party integration. Users can interact with the Snyk platform programmatically via the API or visually via its Web UI, enabling admins to access reports, issues, settings, help, dependencies, etc.

Snyk also provides risk-based prioritization of vulnerabilities, using signals like exploit maturity and reachability to help teams focus on the most critical issues first.

Why Choose Snyk Over Veracode

Snyk scans source code directly, as it's being written, and Snyk says its average scan speeds are 2.4 times faster than similar solutions. Veracode still requires compiled artifacts for some languages, such as .NET, C/C++, and Ruby.

Both vendors use AI for remediation. Snyk’s DeepCode AI combines symbolic and generative AI, and Snyk says it’s never trained on customer data. Veracode Fix applies expert-written reference patches using AI models trained on Veracode’s curated data.

Snyk also offers a free plan and a self-serve Team plan, while Veracode is quote-based. In the IDE, Veracode’s VS Code extension runs scans on demand rather than as you type, and Veracode’s older Greenlight IDE scanner reaches end-of-life on December 31, 2026.

3. SonarQube

Sonar (formerly SonarSource) offers a good Veracode alternative called SonarQube. It’s available as self-managed SonarQube Server and as SonarQube Cloud (formerly SonarCloud), a SaaS service. It focuses on static analysis (it doesn’t do dynamic testing), enabling teams to analyze and measure source code quality and security continually.

Key features:

1) Powerful static code analyzer: SonarQube detects and eliminates bugs, vulnerabilities, and code smells in 40+ languages and frameworks, including Java, Python, Go, JavaScript, TypeScript, C, C++, and C#.

2) Easy integration with popular tools: SonarQube supports integration with multiple Git providers and CI/CD platforms, allowing you to maintain code quality and security in your projects. Its free IDE extension, SonarQube for IDE (formerly SonarLint), and other integrations let you fix issues in real time within your IDE and CI/CD workflow, so you can focus on keeping new code clean.

3) AI-assisted coding: SonarQube for IDE uses Sonar’s rules to flag code smells, bugs, and security vulnerabilities in human-written and AI-generated code as you code. AI CodeFix suggests LLM-generated fixes, AI Code Assurance applies stricter checks to projects containing AI-generated code, and Sonar has added AI agents, such as the SonarQube Remediation Agent, which opens fix pull requests, plus an MCP server for AI coding tools.

4) Secrets detection: SonarQube includes a secret detection tool to prevent secrets from leaking out and becoming a serious security breach.

Beyond these, you might also want multi-language support, issue tracking, dashboards, CI/CD integrations, and rule sets. It provides a centralized dashboard where you can view test coverage imported from your test tools for valuable insights into your code's health.

SonarQube Server comes in Developer, Enterprise, and Data Center editions, alongside the free, open-source SonarQube Community Build. SonarQube Cloud offers Free, Team, and Enterprise plans, plus a free plan for open-source projects.

For code analysis, you can connect SonarQube to a local working copy of a codebase or a DevOps platform like GitHub, GitLab, Bitbucket, or Azure DevOps and specify a project in the repository to analyze.

Why Choose SonarQube Over Veracode

The answer to which tool to choose depends on what you want out of the tool. Veracode specializes in identifying security vulnerabilities, while SonarQube covers both code quality and security. Each of these tools has different specialties.

SonarQube’s security features include taint analysis, secrets detection, IaC scanning, and, with the SonarQube Advanced Security add-on, SCA. Veracode, however, offers security testing that SonarQube doesn’t, including DAST, penetration testing, and ASPM.

4. DeepSource

DeepSource is another solid Veracode alternative for teams seeking a robust platform to keep their codebase secure. It addresses code quality and security concerns and describes itself as an AI code review platform. Harness acquired DeepSource in September 2026; DeepSource Cloud remains available, and its capabilities are set to become part of the Harness platform over time.

Key features:

1) Comprehensive security tools: DeepSource offers static analysis, IaC analysis, SAST, secrets detection, open-source dependency scanning (SCA), and code coverage. It runs 17 core analyzers with 5,000+ deterministic rules, plus an AI review agent, on every pull request to help you address code quality and security issues pre-merge.

2) Developer-friendly experience: The platform integrates with popular DevOps platforms such as GitHub, GitLab, Bitbucket, and Azure DevOps Services. It offers intuitive user dashboards and an MCP server that brings its findings into AI coding agents.

3) Low false positive rate: DeepSource says it guarantees less than 5% false positives on its deterministic analysis. Its analysis post-processing framework is designed to filter out the noise and surface only the most relevant issues.

4) AI-assisted code fixes: DeepSource’s Autofix AI uses large language models to generate fixes for detected issues. You can review the diff before accepting a fix, and Autofix can open pull requests with the changes.

DeepSource offers reports for industry standards like OWASP Top 10, SANS Top 25, and major Common Weakness Enumeration (CWE) issues. It provides a centralized dashboard to help users visualize key metrics, uncover code health trends, and identify improvement areas.

Users can create sophisticated gating rules based on issue categories and severities to block pull requests if any rules are violated. In addition, you can run popular code formatters like Black, Prettier, gofmt, isort, and autopep8 on pull requests, and DeepSource adds a commit with the formatting fixes.

Why Choose DeepSource Over Veracode

DeepSource’s free Open Source plan covers unlimited public repositories, which is useful for open-source projects; private repositories need the paid Team plan. Veracode has no free tier: it deprecated its free Security Labs Community Edition in July 2026 and offers time-limited trials instead.

Both tools report low false-positive rates for their static analysis. DeepSource adds code quality review, while Veracode might be a better option for teams looking for a more holistic solution that addresses various aspects of application security, including DAST, penetration testing, and risk management.

5. Checkmarx

Checkmarx’s application security platform, Checkmarx One, is a robust Veracode alternative for teams seeking a unified code security solution. It provides a complete set of enterprise AppSec solutions for businesses to secure their applications from initial code development to cloud deployment.

Key features:

1) Comprehensive security coverage: The Checkmarx One platform offers code security (SAST, secrets detection, IaC security, and API security), supply chain security (SCA, malicious package protection, container security, repository health, and SBOM), AI supply chain security, and DAST. With these tools, enterprises can secure their application development process–from code to cloud–on a unified platform.

2) Seamless developer experience: Checkmarx One meets developers where they are, offering more than 40 integrations for popular IDEs, SCM tools, bug ticketing systems, and CI/CD platforms. This allows developers to incorporate code security analysis into their workflows without needing to learn new tools.

3) AI Security: Checkmarx One Assist agents bring AI into security workflows. Developer Assist scans code in real time in the IDE and works alongside AI coding assistants like GitHub Copilot, Cursor, and Windsurf, while Triage Assist and Remediation Assist help prioritize and fix findings.

4) Cloud insights: Checkmarx One Cloud Insights, part of its ASPM capabilities, correlates data throughout the entire software development lifecycle (SDLC) and runtime environments. By integrating with cloud security tools, it helps developers concentrate on the most critical issues that significantly impact their business.

In addition, Checkmarx One offers multi-language support, personalized security training (Codebashing), a comprehensive documentation/resource center, and a unified view of risk across your projects.

Checkmarx One also offers end-to-end API security, which helps discover and protect every API in your source code.

Why Choose Checkmarx Over Veracode

With Checkmarx, a single event, such as a push or pull request, can trigger multiple scans (for example, SAST, SCA, and IaC) at once.

Checkmarx also offers self-hosted and on-premises deployment, which Veracode (SaaS-only) doesn’t, and its Developer Assist agent scans in real time in the IDE, while Veracode’s VS Code extension scans on demand.

Choose The Right Option For Your Team

When keeping code secure and clean, the best option is usually the one that leaves no stone unturned when evaluating your code. While most platforms on this list offer a comprehensive set of code quality and security tools, in our view Codacy comes out on top with its developer experience, broad toolchain, and seamless integration into existing workflows.

Each code security platform has its pros and cons. We did our best to highlight these to give you a holistic perspective. Ultimately, the best tool is the one that fits your team’s specific needs and enhances your development process.

A Guide to DevSecOps Tools

Codacy — Fri, 30 Oct 2026 18:06:25 +0000

It’s easy to talk about shifting security left. The idea that you want to bake any security concepts directly into the software development lifecycle (SDLC) from the beginning makes complete sense.

But then you have to do it. This burden falls on your core development team when they have a myriad of other considerations to deal with. Not only do they have to build out your core business logic, consider the final user experience, and ensure the application's performance, but they also have to consider security, compliance, testing, and deployment.

This is where DevSecOps tools come into play. These tools help automate and integrate security practices into the development workflow, making it easier for developers to address security concerns without sacrificing speed or efficiency.

We asked experts for their insights to help you build the proper infrastructure for your team and ensure you can continue high development velocity while delivering secure code and applications.

What is DevSecOps?

Before we look at the tools, what is DevSecOps?

DevSecOps, short for Development, Security, and Operations, is an approach that integrates security practices into the DevOps software development lifecycle. It aims to embed security as a shared responsibility throughout the entire IT lifecycle, from initial design through integration, testing, deployment, and software delivery.

Critical aspects of DevSecOps include:

Shift-left security: Integrating security practices early in the development process rather than treating it as an afterthought.
Automation: Implementing automated security tools and processes to ensure consistent and reliable security checks.
Continuous monitoring: Constantly monitoring the application and infrastructure for potential vulnerabilities and threats.
Collaboration: Encouraging communication and collaboration between development, security, and operations teams to address security concerns effectively.
Security as code: Treating security configurations and policies as code, making them version-controlled, testable, and auditable.
Compliance: Ensuring the software development process meets relevant security standards and regulations.

By adopting DevSecOps practices, organizations can deliver secure software faster, reduce the risk of security breaches, and foster a culture of shared responsibility for security across development, operations, and security teams.

An expert we chatted with reiterated that security is more than just buying tools. The strongest security programs are built on culture.

“At OSP Labs, embedding security into our DevOps workflow was not just a technical decision, it was a cultural shift. We shifted security left, integrating it from the very beginning of development. Shared dashboards and real-time security alerts helped create transparency, ensuring every team had visibility into potential risks. We also embraced Security as Code, automating policies within version-controlled repositories to maintain consistency. When security is part of how you build—not an afterthought—everything changes." — Riken Shah, Founder & CEO OSP Labs.

What’s Required From DevSecOps Tools?

So, DevSecOps tools must consider automation, monitoring, collaboration, security, and compliance. This means there are some essential requirements you want from your DevSecOps tooling.

Integration

DevSecOps tools should seamlessly integrate with the existing development workflow, CI/CD pipelines, and other tools used by the team, such as:

Version control systems (e.g., Git, SVN)
Issue tracking and project management tools (e.g., Jira, Trello)
Continuous integration and continuous deployment (CI/CD) pipelines (e.g., Jenkins, GitLab CI, Azure DevOps)
Containerization and orchestration platforms (e.g., Docker, Kubernetes)
Cloud platforms (e.g., AWS, Azure, GCP)

For example, a static code analysis tool should be able to scan code directly from the version control system. In contrast, a container security tool should integrate with the container registry and orchestration platform. This ensures that security checks are automated and embedded into the development process without causing disruption or delays.

A security tool should never feel like an extra step. If your developers have to stop what they’re doing just to use the tool, you’ve already lost.

“I recently implemented real-time security scanning in our pipeline at Lusha, which caught several vulnerabilities before they hit production and saved us countless hours of manual review. The key was choosing tools that integrated smoothly with our existing Slack notifications and JIRA workflow, making it easy for our dev and security teams to stay in sync without disrupting their normal processes. If your security tools feel like extra work, you’ve picked the wrong ones." — Yarden Morgan, Director of Growth Lusha.

Automation

The core functionality of DevSecOps tools is the automation of security tests, scans, and other processes:

Static application security testing (SAST) to identify vulnerabilities and coding issues
Dynamic application security testing (DAST) to find runtime vulnerabilities
Software composition analysis (SCA) to manage the security of open-source dependencies
Infrastructure as code (IaC) security to ensure secure provisioning and configuration
Compliance checks against industry standards (e.g., OWASP, NIST, PCI DSS)

Automation should be achieved through scriptable APIs, command-line interfaces (CLIs), and integration with CI/CD pipelines. This triggers security checks automatically on code commits, pull requests, or deployment events. This helps ensure that security checks are performed consistently and reliably, reducing the risk of human error and saving time for the development team. Our 2024 State of Software Quality report found that automating is still not a given in software development, with over 40% of teams still conducting unit and frontend testing manually.

Scalability

A security tool that works well for 10 developers might fall apart when you scale to 200.

"Scalability and usability are primary concerns in the selection of DevSecOps tools. It is of utmost importance for the tools to have the capability to scale with workload expansion. I once saw an organization implement a security tool that worked well for a small team but completely broke down when they scaled. If security doesn’t scale with your company, it becomes a bottleneck instead of an enabler." — Sos Denes, Software Developer Rabit Solutions Zrt.

As the application and infrastructure grow, DevSecOps tools should be able to scale accordingly. This includes:

Supporting multiple programming languages and frameworks
Scanning large and distributed codebases efficiently
Handling high volumes of security events and alerts
Integrating with cloud-native technologies and serverless architectures
Providing distributed and parallel processing capabilities

They should be capable of handling increased workloads, larger codebases, and more complex environments without compromising performance or accuracy. Scalability can be achieved through multi-threading, distributed computing, and containerization. Tools such as auto-scaling groups and Kubernetes clusters should also leverage cloud-native scaling mechanisms.

Comprehensive Coverage

They should be able to identify and address security issues across different application stack layers.

Application code (e.g., server-side, client-side, mobile)
Application dependencies and libraries
Containers and container images
Infrastructure as code (e.g., Terraform, CloudFormation)
Cloud resources and configurations
Network and API endpoints

Tools should use various techniques to identify vulnerabilities, such as signature-based detection, behavioral analysis, and machine learning. They should also stay up-to-date with the latest threat intelligence and vulnerability databases.

Actionable Insights

DevSecOps tools should provide clear, actionable insights and recommendations. They should identify security vulnerabilities and offer guidance on how to remediate them effectively.

Detailed vulnerability reports with severity ratings and exploit potential
Remediation guidance with code examples and best practices
Integration with issue-tracking systems for easy assignment and tracking
Risk scoring and prioritization based on business impact and likelihood
False positive management and tuning capabilities

Insights should be presented in a developer-friendly format, such as IDE plugins, chat notifications, or pull request comments. This helps developers quickly understand and address security issues without disrupting their workflow.

Collaboration and Communication

The tools should foster collaboration and communication between development, security, and operations teams.

Security teams can’t just be gatekeepers—they need to work alongside developers. The moment security becomes a team sport, it stops being a blocker.

"I’ve found that fostering collaboration in DevSecOps starts with breaking down the ‘us vs. them’ mentality between security and development teams. At GameTop, we implemented shared Slack channels and weekly security sync-ups where developers and security folks work together to review findings and discuss solutions, not just throw issues over the wall. When security teams are active participants in sprint planning and retrospectives, security is no longer an afterthought—it’s an enabler." — Serge Eliseeff, Founder & CEO GameTop.

DevSecOps tools should facilitate cooperation and communication through:

Role-based access control (RBAC) to manage user permissions and responsibilities
Integrations with communication platforms (e.g., Slack, Microsoft Teams)
Centralized dashboards and reporting for a unified view of security posture
APIs for data sharing and integration with other tools and systems
Workflow management features for tracking and resolving security issues

Tools should enable a collaborative approach to security, where developers, security teams, and operations work together to identify and mitigate risks. They should provide a centralized platform for sharing security findings, tracking issues, and discussing remediation strategies.

One expert we spoke to asked this important question: most security tools can find vulnerabilities—but how well do they help fix them?

"It’s important to remember that almost all scanning platforms can show the same results—the hard part is actually doing something about them. This is where it's not just about the number of integrations, but the quality of them. Not all Jira, GitHub comments, or IDE plugins are built the same, and minor differences end up having a huge impact down the line." — James Berthoty, CEO Latio Tech.

By selecting DevSecOps tools that meet these technical requirements, organizations can ensure a robust, scalable, and effective implementation of security within their development lifecycle.

14 Main Types of DevSecOps Tools

DevSecOps tools are really a series of tools, each focusing on a specific aspect of security within the software development lifecycle. These tools work together to create a comprehensive security framework that addresses vulnerabilities and risks at various stages of development, testing, and deployment.

Type of DevSecOps Tool	Description	Key Features	Codacy Integration (if applicable)
Static Application Security Testing (SAST)	Scans source code or compiled code without executing it to detect vulnerabilities like SQL injection, XSS, and insecure cryptography.	Pattern matching, data flow analysis, taint analysis, IDE and CI/CD integration.	Supports Opengrep, PMD, Checkov, SonarC#, ESLint, and other analyzers.
Dynamic Application Security Testing (DAST)	Tests running applications by simulating attacks and interacting with exposed interfaces to identify runtime vulnerabilities.	SQL injection, XSS testing, no source code access needed, works with custom-built and third-party apps.	App scanning powered by ZAP (formerly OWASP ZAP) on the Business plan; scans start from the Codacy UI or the Codacy API.
Software Composition Analysis (SCA)	Manages risks from open-source and third-party components by scanning for known vulnerabilities, outdated libraries, and license issues.	Maintains vulnerability databases, compares BOM against vulnerabilities, CI/CD and package manager integration.	Powered by Trivy for automatic repository scanning, including malicious package detection.
Infrastructure as Code (IaC) Security	Ensures secure infrastructure provisioning by scanning IaC files for misconfigurations, insecure settings, and policy violations.	Static analysis of IaC files, compares against security benchmarks, blocks insecure deployments.	Scans Terraform, CloudFormation, Kubernetes, and Dockerfiles with Checkov, Opengrep, and Hadolint.
Secrets Detection	Detects and prevents the exposure of sensitive data like API keys and passwords in code repositories and artifacts.	Regular expressions, entropy analysis, machine learning, integrates with VCS and CI/CD pipelines.	Uses Opengrep, Trivy, Checkov, PMD, Bandit, and Prospector for automatic secret detection.
Cloud Security Posture Management (CSPM)	Continuously monitors cloud infrastructure security, analyzing resource configurations for risks and compliance issues.	API-based data collection, compares configurations against best practices and compliance frameworks.	No direct Codacy integration; open-source options include Prowler and ScoutSuite.
Penetration Testing	Simulates real-world attacks to identify vulnerabilities in applications and infrastructure, testing security controls.	Tools for surveillance, vulnerability scanning, exploitation, and post-exploitation activities.	Available on request through a third-party partner, billed separately; results appear in Codacy’s security dashboard.
CI/CD	Automates the building, testing, and deployment of applications to streamline and secure the software delivery process.	Manages code integration, automates testing and deployment, enforces code standards.	Integrates with GitHub, GitLab, and Bitbucket, runs in CI/CD pipelines through its CLI, and can block PRs that don’t meet quality gates.
Compliance and Governance Management	Helps organizations adhere to security standards and regulations, with automated tools for audits and risk assessments.	Security audits, risk assessments, continuous compliance checks, security awareness training.	Supports compliance with automated SAST, SCA, and DAST, remediation deadlines (SLAs), and exportable evidence such as SBOMs.
Vulnerability Management	Identifies, assesses, and prioritizes vulnerabilities, tracking fixes and ensuring timely patching.	Automates scanning and assessment, provides risk prioritization, tracks remediation progress.	Collects findings from Trivy, ZAP-powered app scanning, Jira, and pen tests in a Security and risk management dashboard.
Identity Management	Manages and controls user identities, handling authentication and authorization.	User identity verification, access controls, integrates with authentication providers.	No direct Codacy integration; popular tools include Auth0, Okta, and OneLogin.
Endpoint Security	Protects devices (endpoints) from threats through tools like antivirus, EDR, and firewalls.	Real-time threat detection, data encryption, automatic software updates, and vulnerability scanning.	No direct Codacy integration.
Incident Response and Forensics	Helps identify, manage, and investigate security incidents, minimizing damage and gathering forensic evidence.	Incident detection, evidence collection, breach impact analysis, chain of custody management.	No direct Codacy integration; SIEM and forensic analysis tools cover this area.
Network Security	Protects networks from threats using firewalls, IDS/IPS, and encryption to secure data in transit.	Monitors network traffic, detects intrusions, scans for vulnerabilities, enforces network policies.	No direct Codacy integration; common open-source tools include Wireshark, Snort, and Suricata.

Static Application Security Testing (SAST)

SAST tools analyze the application's source code or compiled code without executing it. They scan the codebase to identify potential security vulnerabilities, such as SQL injection, cross-site scripting (XSS), buffer overflows, and insecure cryptographic practices.

SAST tools use techniques like pattern matching, data flow analysis, and taint analysis to detect vulnerabilities. They support various programming languages and integrate with IDEs, version control systems, and CI/CD pipelines.

Codacy uses open-source and third-party analyzers to perform automatic static code analysis across over 40 supported languages. Supported tools include Opengrep (an open-source fork of the Semgrep engine, which replaced Semgrep in Codacy in February 2026), PMD, Checkov, SonarC#, and ESLint.

Dynamic Application Security Testing (DAST)

DAST tools test the application while it is running in a production-like environment. They simulate attacks and malicious user behavior to identify runtime vulnerabilities and weaknesses.

DAST tools interact with the application through its exposed interfaces, such as web pages, APIs, and web services. They perform actions like SQL injection, XSS, and parameter tampering to uncover security flaws. DAST tools do not require access to the application's source code and can test custom-built and third-party applications.

Codacy's DAST capability, App scanning, is powered by ZAP, formerly OWASP ZAP. ZAP left OWASP in September 2023 and has been branded “ZAP by Checkmarx” since September 2024, when Checkmarx hired its three project leaders; it remains open source under the Apache 2.0 license. App scanning is available on Codacy’s Business plan and can scan web applications and APIs (OpenAPI or GraphQL). You can start scans from the Codacy UI or trigger them through the Codacy API, for example each time you deploy, and see the results as findings in Codacy.

Software Composition Analysis (SCA)

SCA tools help manage the security risks of using open-source and third-party components in an application. They scan the application's dependencies, libraries, and frameworks to identify known vulnerabilities, outdated versions, and license compliance issues.

SCA tools maintain a database of known vulnerabilities and compare the application's bill of materials (BOM) against this database. They provide alerts and recommendations for updating or patching vulnerable components. SCA tools integrate with package managers, build tools, and CI/CD pipelines.

Codacy’s SCA feature is powered by Trivy, the open-source scanner from Aqua Security. Codacy scans your repositories’ dependencies for known vulnerabilities and malicious packages, and on the Business plan it re-scans all repositories daily against newly published CVEs.

Infrastructure as Code (IaC) Security

IaC security tools ensure the secure provisioning and configuring of infrastructure resources defined through code. They scan IaC files, such as Terraform templates or CloudFormation scripts, to identify misconfigurations, insecure settings, and policy violations.

IaC security tools use static analysis techniques to parse and compare the code against best practices and security benchmarks. They provide recommendations for remediation and can block the deployment of insecure infrastructure.

Codacy scans Terraform, CloudFormation, Azure Resource Manager templates, Serverless Framework, and Kubernetes files for misconfigurations with Checkov and Opengrep, and checks Dockerfiles with Hadolint.

Secrets Detection

Secrets detection tools help identify and prevent the exposure of sensitive information in code repositories, configuration files, and other artifacts, such as passwords, API keys, and certificates. They scan the codebase, commit history, and storage locations to detect patterns that resemble secrets.

Secrets detection tools use techniques like regular expressions, entropy analysis, and machine learning to identify potential secrets. They can integrate with version control systems, CI/CD pipelines, and secret management solutions to automate the detection and remediation process.

Codacy’s secret detection spans multiple programming languages and IaC platforms, thanks to its integration with Opengrep, Trivy, Checkov, PMD, Bandit, and Prospector. It scans your code and IaC files for exposed hardcoded secrets and reports them as security findings.

Cloud Security Posture Management (CSPM)

CSPM tools continuously monitor and assess the security posture of an organization's cloud infrastructure. They discover cloud resources, analyze their configurations, and identify security risks and compliance issues. CSPM tools use APIs provided by cloud providers to collect data about resources, such as virtual machines, storage buckets, and network settings.

They compare the actual configurations against security best practices, compliance frameworks, and organizational policies. CSPM tools provide visibility into the overall security posture, generate alerts for misconfigurations, and offer remediation guidance.

Codacy doesn’t include a CSPM tool. Open-source CSPM tools worth considering include Prowler and ScoutSuite.

Penetration Testing

Penetration testing, or "pen testing," involves simulating real-world attacks to identify vulnerabilities in an application or infrastructure. Pen testing tools automate and assist in the process of testing a system's security.

They include tools for surveillance, vulnerability scanning, exploitation, and post-exploitation activities. Pen testing tools can be used to test web applications, networks, APIs, and mobile apps. They help identify weaknesses in authentication, authorization, input validation, and other security controls.

Codacy offers penetration testing on request, billed separately. The manual tests are carried out by a third-party partner, WorkNest Secure, the cyber security division that now includes Bulletproof, and the results appear as findings in Codacy’s Security and risk management dashboard.

Continuous Integration/Continuous Delivery (CI/CD)

CI/CD are practices used in modern software development to automate and streamline the process of building, testing, and deploying applications. CI is about frequently integrating code changes into a shared repository, while CD deals with deploying working changes to a production environment.

CI/CD tools automate and manage these processes, helping teams deliver high-quality software faster.

Codacy integrates with GitHub, GitLab, and Bitbucket, and the Codacy Analysis CLI can run inside CI/CD pipelines. It can even be configured to block a pull request (PR) from merging if it’s not up to standards, for example when it introduces new security issues above a severity threshold you set.

Compliance and Governance Management

Compliance means adhering to standards and regulatory requirements established by governing bodies. Organizations in many industries must comply with stipulated standards and implement security controls to protect their customers’ data. Governance ensures transparency and accountability in decision-making.

Codacy supports compliance work with automated SAST, SCA, and DAST scanning. Each security finding gets a remediation deadline based on its severity, which admins can configure, and findings, analysis history, and an SBOM can be exported as evidence for frameworks such as NIST SSDF.

Vulnerability Management

Vulnerability management involves identifying, assessing, prioritizing, and mitigating security weaknesses in software to reduce exploitation risks. VM tools automate these processes and enable organizations to prioritize fixes based on severity, track progress, and ensure timely patching.

Codacy offers a range of vulnerability scanning tools. To help with vulnerability management, its Security and risk management dashboard collects findings from Codacy’s own scans (including Trivy-based dependency scanning and ZAP-powered app scanning), from a connected Jira instance, and from penetration tests, so teams can prioritize and track vulnerabilities across their repositories.

Identity Management

Identity management is the process of overseeing and controlling user information within computer systems and applications. It includes the mechanisms that verify the user’s identity (authentication) and define their access rights (authorization).

Identity management tools ensure that only authorized individuals can access specific data and perform actions within a system. Some popular identity management tools are Auth0 and Okta (both from Okta, Inc.), OneLogin, Ping Identity (which has combined with ForgeRock), and the open-source OpenAM, now maintained by the Open Identity Platform community.

Endpoint Security

Endpoint security protects devices such as computers, smartphones, and tablets (aka “endpoints”) from cyber threats.

This is achieved through a combination of security tools, policies, data encryption, and timely software updates to address vulnerabilities. Install antivirus software, firewalls, and endpoint detection and response (EDR) systems to keep your endpoints secure.

Incident Response and Forensics

Incident response involves identifying, managing, and mitigating security breaches or cyberattacks. A strong incident response plan enables organizations to react effectively to cyberattacks and minimize disruptions or damages.

Forensics and incident response tools help analyze, track, and resolve security incidents. These tools assist in gathering digital evidence, identifying the source and impact of breaches, and maintaining a clear chain of custody.

Tools like SIEM (Security Information and Event Management) systems, network analyzers, and data recovery software aid in investigating incidents. They also provide critical insights for improving future security measures and compliance with regulatory requirements.

Network Security

Network security focuses on protecting computer networks from cyberattacks, unauthorized access, and data breaches. It involves implementing measures like firewalls, intrusion detection systems (IDS), and encryption to safeguard data transmitted over networks.

Network security tools help detect, prevent, and respond to cyber threats across a network. Open-source tools such as Wireshark (for network traffic analysis), Snort (for intrusion detection), and Suricata (for real-time intrusion detection and prevention) offer robust protection. Other popular tools include OpenVAS (for vulnerability scanning) and pfSense (for firewall management).

By integrating these specialized tools into the DevOps workflow, organizations can automate security checks, identify and remediate vulnerabilities early, and ensure that security is consistently incorporated throughout the development process.

This shift-left approach to security enables teams to catch and fix issues before they make it into production, reducing the risk of security breaches and improving the overall quality of the software.

10 Best Practices For Implementing DevSecOps Tools Into Your Development Workflow

Integrate security from the start. Include security requirements and considerations in the development process's initial planning and design phases. Conduct threat modeling sessions to identify potential security risks and define appropriate controls. Incorporate security user stories and acceptance criteria into the development backlog.
Automate security testing. Integrate SAST tools into the IDE or code editor to provide real-time feedback to developers as they write code. Configure SAST and DAST tools to run automatically as part of the CI/CD pipeline, triggered by code commits or pull requests. Establish quality gates in the pipeline to prevent the deployment of code that fails security tests or exceeds predefined risk thresholds. Use SCA tools to automatically scan for vulnerabilities in third-party dependencies and generate alerts for necessary updates or patches.
Adopt a microservices architecture. Break down monolithic applications into smaller, loosely coupled microservices. Implement security at the microservice level, using techniques like mutual TLS authentication, API gateways, and service mesh for secure communication between microservices. Use containers and container orchestration platforms like Kubernetes to provide isolation and granular security controls for each microservice.
Implement infrastructure as code. Define and manage infrastructure resources using declarative configuration files (e.g., Terraform, CloudFormation). Integrate IaC security tools to scan and validate the configuration files for security best practices and compliance requirements. Use version control systems to track changes to infrastructure code and enable collaboration and review processes.
Secure the CI/CD pipeline. Implement access controls and authentication mechanisms for the CI/CD pipeline components and use secrets management tools to store and manage sensitive information used in the pipeline securely. Implement pipeline security features like signed commits, two-factor authentication, and RBAC. Regularly audit and monitor the pipeline for suspicious activities or unauthorized changes.
Implement continuous monitoring. Deploy CSPM tools to continuously monitor the security posture of cloud infrastructure and detect misconfigurations or policy violations. Integrate security information and event management (SIEM) systems to aggregate and analyze security logs from various tools and systems. Set up automated alerts and notifications for critical security events or anomalies. Regularly review and respond to security findings and prioritize remediation efforts based on risk severity.
Foster a culture of collaboration. Establish cross-functional teams that include developers, security professionals, and operations staff. Encourage regular communication and knowledge sharing through Slack, Microsoft Teams, or shared wikis. Conduct security awareness training sessions to educate developers about secure coding practices, common vulnerabilities, and the importance of security in the development process. Implement a blameless postmortem process to learn from security incidents and continuously improve the development and security practices.
Integrate security into the feedback loop. Incorporate security findings and vulnerabilities into the issue tracking system (e.g., Jira, GitHub Issues). Prioritize and track the remediation of security issues alongside other development tasks. Provide clear guidance and code examples for fixing identified vulnerabilities. Celebrate and recognize the efforts of developers who proactively address security concerns and contribute to a secure development culture.
Implement threat intelligence. Subscribe to threat intelligence feeds and integrate them into the DevSecOps workflow. Use threat intelligence data to prioritize and inform security testing efforts. Regularly update security tools and vulnerability databases with the latest threat information. Conduct red team exercises and penetration testing to simulate real-world attack scenarios and identify gaps in the security posture.
Continuously measure and improve. Establish key performance indicators (KPIs) and metrics to measure the effectiveness of DevSecOps practices (e.g., time to fix vulnerabilities, number of security incidents, code coverage by security tests). Regularly review and analyze these metrics to identify areas for improvement and track progress over time. Conduct security audits and assessments to validate the implementation of security controls and identify opportunities for enhancement. Continuously iterate and refine the DevSecOps processes based on feedback, lessons learned, and industry best practices.

Organizations can build a robust and secure software development lifecycle by following these best practices and integrating DevSecOps tools throughout the development workflow.

Making security an integral part of the development process requires combining technical solutions, processes, and cultural shifts. Continuous collaboration, automation, and measurement are essential to successfully implementing DevSecOps and delivering secure applications at scale.

Where Codacy Fits in a DevSecOps Toolchain

Codacy covers several of the categories outlined above. Out of the box, you get SAST, SCA, IaC scanning, and secret detection. The Business plan adds container image scanning and DAST powered by ZAP, and penetration testing is available separately through WorkNest Secure. Codacy doesn’t cover CSPM, identity management, endpoint security, incident response, or network security, so you’ll need dedicated tools for those.

Codacy integrates with your existing development workflow through GitHub, GitLab, and Bitbucket, and supports more than 40 languages. It automates security testing throughout the development process, with feedback on pull requests and, through Codacy Guardrails, in the IDE and inside AI coding agents.

It identifies vulnerabilities in code, dependencies, containers, and infrastructure as code, and collects the results in its Security and risk management dashboard, where each finding gets a severity-based remediation deadline.

Codacy also provides security reports, SBOM exports, and a Jira integration, so development, security, and operations teams can work from the same list of findings.

Bringing these capabilities into a single platform can simplify the implementation of security best practices, alongside the dedicated tools you use for the categories Codacy doesn’t cover.

DevSecOps Tools FAQs

1. What should teams prioritize when selecting DevSecOps tools?

If a security tool doesn’t fit into your existing workflow, your team won’t use it. The goal is to enhance security without slowing development down.

"The most important criteria, and usually the deciding factor when selecting DevSecOps tools, is compatibility with CI/CD. However, API integration capability is key as well. Organizations must also consider ease of automation, the ability to scan multiple environments, clear reporting, and support for existing code. If the tool you're looking at checks all of these boxes, you'll have a smooth transition and integration into your existing pipelines."— Bill Mann, Privacy Expert Cyber Insider.

2. How can teams automate security without disrupting developers?

Security automation should feel like a safety net, not an obstacle course. When done right, it catches vulnerabilities early without creating extra work for developers.

"One of the most effective ways to ensure security without slowing down development is to integrate automated security checks at the earliest stages of coding. We implemented pre-commit hooks in our IDEs, ensuring that security scans run before the code even reaches the pipeline. This proactive approach prevents costly fixes down the line and keeps security from becoming a last-minute hurdle." — Stanislav Khilobochenko, VP of Customer Services Clario.

3. What are common mistakes companies make when implementing DevSecOps?

Many teams rush into DevSecOps without a strategy. They treat security as an afterthought instead of what it is— an integrated part of development. This leads to frustration, inefficiency, and security gaps.

"Working in healthcare software, I’ve found that phasing in DevSecOps tools gradually—starting with automated dependency scanning and slowly adding container security—helped our teams adapt without feeling overwhelmed. We made sure to pick tools that had strong compliance reporting features and could integrate with our existing Epic systems, which made a huge difference in getting buy-in from both technical and clinical stakeholders." — Devon Mobley, Chief Growth Officer Calvient.

4. How can organizations ensure their security tools scale with growth?

A security tool that works for a small team today might collapse under the weight of a growing organization.

"When selecting DevSecOps tools, I focus on usability, scalability, and how well the tools integrate with our existing workflows. I learned this through trial and error when we once adopted a tool that required extensive training and disrupted the team's productivity. Now, I prioritize solutions that are intuitive, have strong documentation, and can seamlessly plug into our CI/CD pipelines without adding complexity." — Hristiqn Tomov, Software Engineer Resume Mentor.

5. How can organizations measure the success of their DevSecOps implementation?

Implementing DevSecOps isn’t just about adding security tools—it’s about improving workflows, reducing risk, and maintaining speed. But how do you measure whether it’s working?

"To truly understand if DevSecOps is making an impact, organizations need to track security effectiveness the same way they track software delivery. We focus on key performance indicators (KPIs) like time to fix vulnerabilities, the number of security issues caught before production, and developer adoption of security tools. The goal isn’t just to find vulnerabilities—it’s to build a development culture where security is second nature." — Nikita Sherbina, Co-Founder & CEO AIScreen.

The Complete Guide to Technical Debt Tracking for Engineering Leaders

Codacy — Fri, 30 Oct 2026 15:04:28 +0000

Every engineering team knows technical debt exists. Far fewer can tell you exactly where it lives, how much it costs, or whether it is growing.

That gap between awareness and visibility is where technical debt tracking comes in. This guide covers what to track, how to measure it, which tools help, and how to build a practice that actually sticks.

What is technical debt tracking

Technical debt tracking is the practice of identifying, categorizing, and prioritizing code compromises, outdated dependencies, and architectural shortcuts in your codebase. Think of it like treating shortcuts as loans: you took them to move faster, but now you owe future work to pay them back. Tracking makes that loan visible, measurable, and systematically payable.

The difference between knowing about debt and tracking it matters more than most teams realize. Knowing means someone on the team remembers a shortcut was taken. Tracking means that shortcut is documented somewhere, assigned a severity, and visible to everyone who makes prioritization decisions.

Without tracking, debt becomes invisible. It compounds quietly until it surfaces as slower feature delivery, harder onboarding, or a security incident that catches everyone off guard.

Why technical debt tracking matters for engineering leaders

Engineering leaders balance competing pressures: shipping features, maintaining quality, managing security risk, and keeping teams productive. Untracked technical debt quietly undermines all four.

The consequences tend to show up gradually, then all at once. A codebase that was fast to work in two years ago now takes twice as long for simple changes. New engineers take months to become productive because the architecture has drifted from any documentation that exists.

Here are the leadership concerns that technical debt tracking directly addresses:

Velocity erosion: Untracked debt silently slows every sprint as developers work around accumulated complexity.
Risk blind spots: Hidden complexity becomes a security and reliability liability that only surfaces during incidents.
Resource misallocation: Without data, teams cannot make a credible case for dedicated remediation time.
Compliance gaps: Auditors increasingly ask for evidence of code quality practices, and "we know about it" is not documentation.

Types of technical debt to track

Technical debt comes in multiple forms. Tracking only one type leaves gaps. A team that monitors code complexity but ignores dependency vulnerabilities has an incomplete picture of their risk exposure.

Code debt

Code debt is the most visible form: duplicated code, code smells, overly complex functions, and inconsistent patterns. Static analysis tools catch most of it, which makes code debt the easiest category to track systematically.

Architectural debt

Misaligned system structure creates friction that compounds over time. Monoliths that have outgrown their design, tight coupling between services, and data flows that no longer match business requirements all fall into architectural debt.

Design debt

Inconsistent APIs, unclear abstractions, and patterns that made sense three years ago but no longer fit current requirements create design debt. Design debt often shows up as confusion during code review or onboarding.

Test and coverage debt

Missing or flaky tests erode confidence in deployments. When teams stop trusting their test suite, they slow down releases or skip testing entirely, both of which create downstream problems.

Documentation debt

Outdated or missing documentation increases tribal knowledge risk. When the engineer who understands a system leaves, the team inherits a black box.

Dependency and security debt

Outdated libraries, known vulnerabilities, and unpatched CVEs represent security risk that compounds over time. Dependency and security debt often gets tracked separately from code quality, which creates visibility gaps.

AI-generated code debt

AI coding tools can introduce debt at scale if their output is not governed. Patterns like unapproved model calls, unsafe code patterns, and missing tests appear more frequently when code generation outpaces review capacity.

How to measure technical debt

Measurement helps turn subjective concerns into measurable signals. Without metrics, conversations about technical debt become opinion battles. With metrics, teams can prioritize based on evidence.

Technical debt ratio

The technical debt ratio compares estimated remediation cost to total development cost. A ratio that trends upward over time signals that debt is accumulating faster than the team is paying it down.

Code complexity and code smells

Cyclomatic complexity measures how many independent paths exist through a piece of code. Duplication percentage and static analysis findings round out the picture. Complexity metrics are straightforward to automate and track over time.

Test coverage gaps

Coverage percentage tells you how much code lacks unit tests. Diff coverage, which measures coverage on new code specifically, serves as a leading indicator. A team with high overall coverage but low diff coverage is accumulating test debt with every merge.

Remediation time and cost

Estimating hours or cost to fix known issues helps prioritize. A high-severity issue that takes an hour to fix is a different conversation than one that requires a two-week refactor.

Defect density and change failure rate

Bugs per lines of code and deployment failure rate are lagging indicators of accumulated debt. By the time defect density and change failure rate spike, the underlying debt has already compounded.

How to track technical debt across a codebase

Tracking technical debt works best as a repeatable workflow, not a one-time audit. The goal is continuous visibility, not periodic cleanup sprints that lose momentum after a few weeks.

1. Establish a consolidated source of truth

Consolidate findings from linters, scanners, and manual notes into one system. Scattered Jira tickets and spreadsheets create fragmented visibility that makes prioritization difficult.

2. Instrument tracking in the IDE

Catching issues at authoring time helps surface new debt earlier in the development workflow. IDE extensions that surface debt as developers write code create a feedback loop that is faster than waiting for CI results.

3. Enforce quality gates at the pull request

Blocking or warning on new debt before merge prevents accumulation. Coverage thresholds, complexity limits, and security scans applied at the pull request level catch issues before they compound.

4. Roll up metrics to an org-level dashboard

Leadership needs aggregate trends, not file-level noise. Unified dashboards enable cross-team comparisons and make it possible to identify which repositories or services carry the most risk.

5. Review debt on a recurring cadence

Schedule monthly or quarterly reviews to reassess priorities and celebrate progress. Without a recurring cadence, debt tracking becomes another abandoned initiative.

How to prioritize technical debt

Not all debt is equal. Some is benign and can wait indefinitely. Some is compounding and will cost more to fix next quarter than it does today.

Impact versus effort scoring

A simple matrix works well for most teams: plot debt items by impact and effort, then address high-impact, low-effort items first. High-impact, low-effort wins build momentum for larger remediation efforts.

Risk-weighted prioritization

Factor in security severity, compliance requirements, and blast radius of failure. A code smell in a rarely-touched utility function is a different priority than a vulnerability in an authentication service.

Tying debt to business outcomes

Connect debt items to revenue features, reliability SLAs, or customer pain. When debt remediation is framed as "this will let us ship the Q3 feature faster," it competes more effectively for sprint capacity.

What to look for in a technical debt tracking tool

Manual tracking works for small teams, but it breaks down as codebases and teams grow. The right tooling automates the tedious parts and surfaces the information that matters.

Coverage across quality and security

A tool that detects code smells but ignores vulnerabilities creates blind spots. Look for platforms that cover static analysis, security scanning, dependency issues, and coverage gaps in one place.

Integration with Git and IDE workflows

The best tools connect directly to your Git provider and surface findings where developers already work. If engineers have to context-switch to a separate dashboard, adoption suffers.

Org-wide visibility and reporting

Dashboards that roll up to leadership make it possible to compare teams and repositories. Exportable reports simplify audit preparation and executive communication.

Noise reduction and false positive handling

High false positive rates train developers to ignore alerts. AI-assisted triage or tunable rules keep the signal-to-noise ratio manageable.

Policy and gate enforcement

The ability to set thresholds and block merges ensures consistent enforcement across teams. Without enforcement, tracking becomes documentation without action.

Platforms like Codacy unify static analysis, security scanning, coverage, and AI code governance in one system, replacing fragmented toolchains with a single source of truth for code quality and security.

How AI-assisted development changes technical debt

AI coding tools accelerate code generation, but they can also accelerate debt accumulation if their output is not governed. The same properties that make AI useful for productivity create new tracking challenges.

Volume: More code means more potential debt surface. A developer using AI assistance might generate three times the code in a day, which means three times the opportunity for shortcuts to slip through.
Provenance: Attributing authorship becomes harder when code is generated, edited, and regenerated. Traditional review workflows assume a human author who can explain their reasoning.
Policy gaps: Existing tools may not detect AI-specific risks like unapproved model calls, subtle security or policy issues in generated code, or patterns that reflect outdated training data.

Tracking AI-generated debt requires visibility into which code came from an AI agent and whether it passed quality and security checks. Without that visibility, teams cannot distinguish between debt they chose to accept and debt they inherited unknowingly.

Connecting technical debt tracking to compliance and reporting

Regulated industries require audit-ready evidence of code quality practices. Technical debt tracking, when done systematically, can contribute to audit evidence when paired with existing controls.

Frameworks where debt tracking evidence applies include:

ISO 27001: Requires documented controls for software development security.
SOC 2: Includes criteria for change management and code quality.
HIPAA: Mandates safeguards for systems handling protected health information.
PCI DSS: Requires secure development practices for payment systems.
NIST 800-218 (SSDF): Defines secure software development practices for federal suppliers.

Continuous tracking turns compliance from a scramble into a dashboard export. When auditors ask for evidence, the answer is already documented.

Building a technical debt tracking practice that sticks

The teams that succeed with technical debt tracking treat it as a system, not a project. A few principles help:

Start with visibility. You cannot manage what you do not measure. Even imperfect tracking is better than none.
Embed tracking into daily workflows. Quarterly audits lose momentum. Pull request gates and IDE feedback create continuous visibility.
Use tooling that consolidates quality, security, and AI governance. Fragmented tools create fragmented visibility.
Tie debt reduction to business outcomes. Executive support follows when remediation is framed in terms of velocity, risk, and revenue.

Technical debt is not a problem to solve once. It is a system to manage continuously. The teams that build that system into their workflows ship faster, onboard engineers more quickly, and spend less time firefighting.

Frequently asked questions about technical debt tracking

How do you track technical debt in agile teams?

Treat debt items as backlog tickets tagged by type and severity, and allocate a fixed percentage of each sprint to remediation. Automated scanning at the pull request keeps new debt from accumulating unnoticed.

What is the 80/20 rule for technical debt?

The 80/20 rule suggests that roughly 80 percent of debt-related pain comes from 20 percent of the codebase. Prioritize that high-impact slice first to get the fastest return on remediation effort.

What is a healthy technical debt ratio?

There is no universal threshold, but many teams aim to keep remediation cost below a small fraction of total development cost. The more important signal is whether the ratio is trending up or down over time.

Should technical debt live in Jira or a dedicated tool?

Either can work, but dedicated tools surface debt automatically from code analysis rather than relying on manual entry. A unified platform that feeds findings into your issue tracker gives you the best of both.

Who owns technical debt tracking in an engineering organization?

Ownership typically sits with engineering leadership or a platform team, but accountability is shared across squads. Clear ownership ensures debt gets prioritized alongside feature work rather than ignored indefinitely.

Top 5 Checkmarx Alternatives in 2026

Codacy — Wed, 28 Oct 2026 20:25:10 +0000

Checkmarx excels at finding and remediating risks across the entire application footprint. Though the platform offers some good products and services, it does have some limitations (e.g., it doesn’t offer code quality analysis), and its quote-based pricing can make it harder for smaller teams to evaluate.

If you’re a Checkmarx user or part of a team seeking a more robust solution for code quality and security management, here’s our list of the top five Checkmarx alternatives for 2026.

What Checkmarx Does

Checkmarx is an enterprise application security company best known for static application security testing (SAST). Its main product is Checkmarx One, which Checkmarx describes as an agentic, AI-powered application security platform. Checkmarx One runs as a cloud service, and Checkmarx also supports self-hosted and on-premises deployments.

The platform offers these key features:

1. Comprehensive security coverage: Checkmarx One offers a wide array of security tools to secure various application components, such as source code (SAST, secrets detection, IaC security, and API security), the software supply chain (SCA, malicious package protection, container security, repository health, and SBOM), AI supply chain security, and running applications (DAST).

2. AI security agents: Checkmarx One Assist includes a Developer Assist agent that scans code, including AI-generated code, in real time in the IDE, plus Triage Assist and Remediation Assist agents that help prioritize and fix findings. Checkmarx’s MCP server also connects Checkmarx One to MCP-compatible AI tools.

3. Seamless developer experience: It integrates with all major Git providers, CI/CD, and bug ticketing systems. It also offers IDE plugins for Visual Studio Code (which also work in Cursor, Windsurf, and Kiro), JetBrains IDEs such as IntelliJ IDEA, Visual Studio, and Eclipse, enabling developers to catch and resolve issues without leaving their IDE window.

4. Malicious package protection: Checkmarx scans open-source packages against its database of more than 550,000 known malicious packages. It alerts developers of any compromised dependency and can block malicious packages before they’re installed.

Beyond these, Checkmarx One offers ASPM, Codebashing secure-code training, a detailed resource center, and compliance reporting for standards like OWASP Top 10, PCI DSS, NIST SP 800-53, and FISMA. Checkmarx says its SAST engine supports 35+ languages.

When evaluating alternatives to Checkmarx, consider the following factors:

Scanning capabilities: Only consider tools that offer the type of security protection your project requires. We recommend a tool that provides static and dynamic application security testing features to ensure comprehensive coverage of potential vulnerabilities.
Workflow integrations: Before picking a tool, check if it’s compatible with your current development and deployment processes, including CI/CD pipelines, source control systems, IDEs, and container registries. Smooth integration can greatly improve the efficiency of your security workflow.
Speed and performance: Look for solutions that provide quick analysis and use resources efficiently, as this improves developer productivity. Also, check how accurately the tool identifies real vulnerabilities versus false positives, as this impacts remediation efforts.
Cost: Analyze the pricing structure, including licensing fees and any additional costs for features or support, to ensure it fits within your budget.

What Checkmarx Misses

So, why might one look beyond Checkmarx for their code security needs? These are some of the main issues and limitations associated with Checkmarx:

It doesn’t do code quality: Although Checkmarx One offers many tools for enforcing security practices, it does not perform code quality analysis. Teams looking for code quality must complement it with a separate code quality solution, potentially complicating the code review process.
False positives: Some reviewers have reported noisy SAST results that need tuning. Checkmarx says its newer Findings Analysis Engine cuts false-positive noise by 60%, but it’s worth testing results on your own codebase.
Cost and pricing transparency: Checkmarx doesn’t publish list prices. Quotes depend on the modules, deployment model, and number of developers, and there’s no self-serve free tier, which can make it harder for smaller teams to evaluate.

Let’s look at some worthy alternatives you should explore in 2026.

Codacy

Codacy is our own product, so it’s first on our list. It offers an integrated platform for code quality and security across enterprise applications and codebases, including AI-generated code. It covers security and code quality at multiple stages of the software development lifecycle while streamlining the code review process via workflow integrations, automated code checks, and other features.

Key features:

1. Comprehensive code analysis: Codacy offers code quality analysis and security scanning to catch a range of problems in code, including OWASP Top 10 vulnerabilities, code smells, code duplication, and maintainability issues. It supports over 40 languages (including infrastructure-as-code and configuration formats) for diverse development teams.

2. Security-focused approach: Codacy's wide array of tools helps mitigate risks. It offers SAST and SCA (including malicious package detection) to keep your code and dependencies secure, secret detection to secure sensitive information, and IaC scanning to detect cloud misconfigurations. On the Business plan, it adds DAST (powered by ZAP) to find vulnerabilities in running web apps and APIs, and container image scanning. Manual penetration testing is available as a separately billed service.

3. Developer-first experience: The platform integrates with GitHub, GitLab, and Bitbucket, Jira for issue sync, and Slack for alerts, and runs open-source analyzers like Opengrep, Trivy, ESLint, PMD, and SonarC#. It offers risk management dashboards, a CLI for local scans, a comprehensive resource center, and a free IDE extension for Visual Studio Code, Cursor, Windsurf, and JetBrains IDEs such as IntelliJ IDEA.

4. AI-assisted reviews and fixes: Codacy’s AI Reviewer adds context-aware feedback and fix suggestions to pull requests on GitHub (Team and Business plans), with AI-enhanced comments available on GitLab and Bitbucket. In the IDE, Codacy Guardrails uses the Codacy MCP Server and CLI to scan AI-generated code so coding agents can fix issues before they’re committed.

5. Automated code reviews: After you import a repository, Codacy automatically scans it for issues using a set of customizable coding standards, best practices, and quality gates. It also analyzes your pull requests and prevents problematic code from merging into the main branch (if you enable status checks and branch protection).

Codacy also tracks code coverage (which measures how much code is being tested) from the coverage reports your test runs produce. It shows coverage metrics on every pull request, allowing teams to track and improve test coverage over time.

The Security and Risk Management overview page offers a high-level view of your organization's security posture. It includes the number of open findings, their severity distribution, a history of resolution, and a breakdown of the highest-risk repositories and most common security categories.

Codacy logs important events in your organization, indicating when your team members execute specific operations. Organization admins and managers can obtain the audit log data of the organization events using the Codacy API (the audit logs feature is only available on the Business plan).

Why Choose Codacy Over Checkmarx

Codacy consolidates security and code quality tools in one place: SAST, SCA, hardcoded secrets detection, IaC scanning, code quality analysis, and code coverage, with DAST and container scanning on the Business plan and penetration testing available separately.

Checkmarx goes deeper in some security areas, such as API discovery, malicious package protection, and self-hosted deployment, but it doesn’t do code quality analysis. Teams looking for code quality must complement it with a separate code quality solution, potentially complicating the code review process.

In addition, Checkmarx pricing is quote-based, which can be a hurdle for smaller teams. Codacy publishes per-developer pricing for its Team plan, offers a free plan for its IDE extension, and allows you to start analyzing code immediately with minimal configuration.

Snyk

Snyk presents a robust alternative to Checkmarx, built around a developer-first approach to application security. It does a good job of finding risks in the various application components and eliminating them.

1. Wide security coverage: Snyk identifies and resolves vulnerabilities in source code (Snyk Code), open-source dependencies (Snyk Open Source), container images (Snyk Container), infrastructure as code configurations (Snyk IaC), running web apps and APIs (Snyk API & Web), and hardcoded secrets (Snyk Secrets).

2. Developer-first design: Though some users have complained about Snyk’s user interface, the tool offers developer-friendly features such as more than 100 integrations (container registries, IDEs, Git, AI coding assistants, CI/CD, etc.), flexible controls, a CLI tool, a detailed API, comprehensive user documentation and a Web UI.

3. AI-assisted coding: Snyk’s DeepCode AI engine, which combines symbolic and generative AI, powers Snyk Agent Fix to suggest security fixes for human-written and AI-generated code. Developers can review suggested fixes directly within their IDE and adjust as needed, and Snyk Studio brings Snyk’s scanning into AI coding assistants like Claude Code and Cursor.

4. Compliance controls: Snyk helps you meet requirements for regulatory compliance, open-source license compliance, and cloud compliance. It supports controls for security training, SBOMs, scanning, vulnerability reporting, limiting access, and configuration standards.

5. Dedicated vulnerability database: The Snyk Vulnerability Database contains a comprehensive list of known security vulnerabilities and is updated by its dedicated security research team.

Beyond these, Snyk offers free security training (Snyk Learn), an API, a detailed documentation/resource center, and a Web UI that provides centralized monitoring, allowing teams to implement governance and compliance with dashboards, policies, and reports.

Snyk retired its standalone Snyk Advisor site in early 2026. Its package health data (popularity, maintenance, security, and community) now lives in the Snyk Security Database for npm, PyPI, and Go packages.

Why Choose Snyk Over Checkmarx

Snyk is easier to adopt for smaller teams: it offers a free plan (covering SCA, SAST, IaC, and container scanning) and a self-serve Team plan, while Checkmarx is quote-based with no self-serve free tier. Both vendors now offer real-time IDE scanning and AI agents for remediation, so compare them on your own codebase.

Snyk also says its scans average 2.4x faster than similar solutions. On the other hand, Checkmarx offers self-hosted and on-premises deployment, which Snyk doesn’t.

DeepSource

DeepSource is another solid Checkmarx alternative for teams looking for reliable static code analysis. Though not a comprehensive solution like Codacy, it offers static code analysis in multiple languages, an AI review agent, auto-remediation technology, and other tools to make the codebase cleaner and more stable. Harness acquired DeepSource in September 2026; DeepSource Cloud remains available, and its capabilities are set to become part of the Harness platform over time.

Key features:

1. Comprehensive security tools: DeepSource offers static analysis, infrastructure as code (IaC) analysis, SAST, secrets detection, open-source dependency scanning (SCA), and code coverage, supporting major programming languages (including JavaScript, Python, PHP, Ruby, and Java).

2. Workflow integrations: The platform can be integrated with GitHub, GitLab, Bitbucket, and Azure DevOps Services. It also provides an MCP server that brings its findings into AI coding agents.

3. Low false positive rate: DeepSource’s SAST tool provides stable results. The company says it guarantees less than 5% false positives on its deterministic analysis, which would make it more accurate than many alternative SAST tools.

4. AI-assisted code fixes: DeepSource’s Autofix generates fixes for all occurrences of an issue and can create pull requests with the recommended changes, and Autofix AI uses large language models to generate fixes, saving users the hassle of manually addressing these issues.

Additionally, DeepSource reviews every pull request and tracks code coverage uploaded from your CI, allowing users to address issues and monitor coverage levels. It also offers a centralized dashboard that enables teams to visualize key metrics, monitor code health trends, and identify improvement areas.

It can run code formatters like Black, Prettier, gofmt, isort, and autopep8 on pull requests to format your code, allowing you to enforce coding standards without lifting a finger.

Why Choose DeepSource Over Checkmarx

DeepSource says it guarantees that less than 5% of the issues its deterministic analysis reports are false positives. It also allows users to mark false positives, which are reported to DeepSource’s engineering team for review.

It’s important to note that Checkmarx and DeepSource aren’t complete tools for holistic application security. DeepSource doesn’t offer DAST or container scanning, and Checkmarx doesn’t offer code quality analysis. Customers looking for comprehensive coverage must complement them with an additional tool or opt for an integrated solution like Codacy.

Veracode

Veracode is an application security platform designed to help software teams build and scale secure software from code to cloud. It offers designed-for-developer tools, API and workflow integrations, and auto-remediation technology developers need to write and ship secure code.

Key features:

1. Wide array of security tools: Veracode offers Static Analysis (SAST), Software Composition Analysis (SCA), Dynamic Analysis (DAST), Penetration Testing as a Service (PTaaS), Container Security, Package Firewall, and Veracode Risk Manager (ASPM, formerly Longbow), making it a noteworthy option for enterprises that want comprehensive security coverage.

2. Workflow integrations: The platform has over 40 integrations into IDEs, CI/CD tools, SCM tools, and ticketing systems. Users can install Veracode Scan plugins for Visual Studio Code, JetBrains IDEs, Visual Studio, or Eclipse to run SAST and SCA scans without leaving their IDE window (Veracode says its static analysis supports more than 100 languages and frameworks).

3. Cloud-native architecture: Veracode leverages the advantages of the cloud with automated application analysis in the pipeline and on-demand expertise. According to Veracode, the platform scales automatically, enabling teams to grow as their business expands.

4. AI-assisted flaw remediation: When you install Veracode in your IDE, it comes with the Veracode Fix feature, which provides AI-generated fixes for flaws. The feature is also available as a command-line tool.

In addition, you get an intuitive user dashboard, support for different application types (including source, binary, and hybrid scanning), and a dedicated e-learning platform. Its reporting gives product owners a birds-eye view of application security.

With Veracode’s policy management and reporting, security teams can set clear goals for software security, get progress reports, and guide development teams on what to fix.

Why Choose Veracode Over Checkmarx

Veracode and Checkmarx offer many security features, but neither is a complete application security platform. Neither tool performs code quality analysis or tracks code coverage, requiring users to complement them with additional tools.

It is worth noting that Veracode can scan compiled binaries as well as source code, offers expert-led manual penetration testing, and reports a false-positive rate under 1.1% for its static analysis. Checkmarx, for its part, offers real-time IDE scanning and self-hosted deployment.

GitGuardian

GitGuardian is a developer-focused secrets and non-human identity (NHI) security platform. It monitors your repositories, as well as public GitHub activity, for exposed API tokens, database credentials, and certificates, ensuring your code is protected through continuous oversight. In addition, the platform offers additional tools to maintain the cleanliness and security of your application code.

Key Features:

1. Real-time monitoring: GitGuardian continuously scans source control, CI/CD pipelines, and collaboration tools for sensitive data such as API keys, database credentials, and certificates. With the ggshield command-line tool, developers can detect 600+ types of secrets before code is pushed, ensuring comprehensive secrets detection before code deployment.

2. Non-human identity security: Beyond detection, GitGuardian’s NHI Governance inventories secrets and machine identities across secrets managers (like HashiCorp Vault and AWS Secrets Manager) and cloud IAM, and its honeytokens help detect intruders in your software supply chain.

3. Automated alerts: When GitGuardian finds exposed sensitive information, it quickly alerts the development team. This helps them fix problems right away, lowering the risk of unauthorized access.

4. Integrations: GitGuardian integrates with GitHub, GitLab, Bitbucket, and Azure DevOps, and its ggshield CLI works with CI/CD platforms including GitHub Actions, GitLab CI, Jenkins CI, CircleCI, and Azure Pipelines. It also provides integrations for Git hooks (scripts that are triggered by certain actions in the software development process, like committing or pushing).

5. Detailed reporting: The platform provides detailed incident information, including the type of secret detected, the files it was found in, and guidance on how to remediate the issue. This helps teams understand and mitigate risks effectively.

GitGuardian’s unified incident management platform enables software teams to centralize incidents across source control and productivity tools like Slack, Jira, and Confluence for a holistic view, facilitating swift remediation across monitored assets.

The platform also uses machine learning, such as its False Positive Remover, to reduce alert fatigue. Users can customize monitoring settings and collaborate with teammates via the dashboard.

Why Choose GitGuardian Over Checkmarx

Checkmarx offers a broad range of security solutions, including static and dynamic application security testing, software supply chain security, and compliance reporting, making it ideal for comprehensive application security coverage.

In contrast, GitGuardian specializes in detecting sensitive information like API keys and passwords in code repositories and beyond, providing effective alerts for any accidental exposure. Checkmarx One includes secrets detection too, but if your focus is on managing secrets and non-human identities, GitGuardian is a strong choice.

Choose The Right Option For Your Team

In our view, Codacy is the best fit for teams that want security, code quality, and test coverage in one platform. Plus, it’s user-friendly and plugs into existing Git workflows without requiring CI pipeline changes for code scanning.

In the end, the choice is entirely yours. This article seeks to showcase the strengths and weaknesses of each tool. The right tool for your team will be the one that best fits your unique requirements and enhances your development workflow.