DEV Community: Dragonsoft DevSecOps

Say Goodbye to "Fixing in Production": Comparing Java Profiling Tools for Real-time Insights.

Dragonsoft DevSecOps — Fri, 24 Apr 2026 07:42:18 +0000

Summary：In Java development, performance issues — if left undetected early — often lead to serious consequences in production: poor user experience, increased deployment costs, or even application downtime. Catching and resolving performance problems early in the development cycle is critical to maintaining efficient delivery.Dragonsoft, as an authorized distributor of Perforce, conducted an in-depth comparison of three mainstream Java performance analysis tools: XRebel, JProfiler, and YourKit. This article evaluates them across use cases, user interface, real-time feedback, IDE integration, and instrumentation overhead — with pricing and trial info included to help Java teams find the right fit.

Identifying memory leaks, threading issues, and inefficient database queries early saves development teams thousands of hours and substantial budget resources. While it’s undeniable that Java performance analysis tools play an important role in the Java code pipeline, which tool is best can depend heavily on use case and individual preference.

Three popular tools dominate the conversation around Java performance analysis: YourKit, JProfiler, and XRebel. Each tool provides specific features tailored to different stages of the software development lifecycle. DevOps engineers, performance engineers, QA engineers, systems engineers, developers, etc. must understand the specific strengths and limitations of these applications to integrate them seamlessly into their Java development practice.

In this blog post, we’ll break down the differences between YourKit, JProfiler, and XRebel side by side. Read on to find out which tool fits best into your development pipeline and helps your team build better Java applications.

YourKit: Advanced Profiling and Memory Analysis

YourKit Java Profiler delivers deep insights into memory analysis and advanced profiling. Java teams turn to this desktop application to diagnose complex memory leaks and performance bottlenecks in large Java applications.

Memory Profiling and Garbage Collection Analysis in YourKit
YourKit excels at memory profiling. It provides detailed heap dumps and deep garbage collection analysis. Teams rely on YourKit to pinpoint exact memory leak locations and understand object allocations. This deep dive into memory ensures applications run without consuming unnecessary resources.

*Thread Analysis in YourKit *
The tool offers comprehensive thread analysis. Java teams use YourKit to identify deadlocks, track contention, and monitor locking issues quickly. This visibility keeps multi-threaded Java applications working properly.

Database Monitoring and Microservice Tracing in YourKit
YourKit includes tracking for SQL queries and JDBC profiling. It also provides web request telemetry for tracing microservices. With YourKit, Java teams gain a clear view of how database calls impact overall application speed.

Feedback and Instrumentation Overhead in YourKit
YourKit offers near real-time feedback through data sampling. The instrumentation overhead to use YourKit is moderate, though users can configure the settings to improve performance during active profiling sessions.

*IDE Integration in YourKit *
Java teams can integrate YourKit directly with IntelliJ IDEA and Eclipse. This integration streamlines the profiling process without forcing developers to leave their coding environment.

JProfiler: In-Depth Development and Runtime Profiling

JProfiler bridges the gap between high-level Java performance analytics and granular JVM data. It provides unmatched insights for resolving tough performance problems during in-depth development and runtime profiling.

Memory Profiling and Garbage Collection Analysis in JProfiler
JProfiler features a robust heap walker and allocation call trees. It includes thorough garbage collection analysis, making the complex process of identifying memory leaks straightforward.

*Thread Analysis in JProfiler *
JProfiler tracks live threads and detects deadlocks. Java teams use these features to track local requests across different threads, ensuring smooth execution paths.

Database Monitoring and Microservice Tracing in JProfiler
JProfiler excels at HTTP and REST tracing, tracking responses across complex microservice boundaries. The tool highlights slow database calls so Java teams can address them quickly.

Feedback and Instrumentation Overhead in JProfiler
The JProfiler user interface displays live profiling data as it happens, although the overall approach leans toward batch-oriented analysis. The instrumentation overhead runs higher than other tools, especially when Java teams run the application in full profiling mode.

IDE Integration in JProfiler
JProfiler integrates with IntelliJ IDEA, Eclipse, NetBeans, and VS Code. This broad IDE support allows Java teams to start profiling with a single click.

XRebel: Real-Time Performance Feedback During Development

XRebel sets itself apart in the Java performance analysis landscape by focusing on addressing performance issues before production. By fixing problems early in the development lifecycle, Java teams can fix performance issues up to 60% faster.

Memory Profiling and Garbage Collection Analysis in XRebel
XRebel focuses on object allocations. This targeted focus helps accelerate performance analysis in development.

Thread Analysis in XRebel
XRebel provides a basic live threads view. This straightforward presentation gives Java teams immediate visibility without overwhelming them with unnecessary data points.

*Database Monitoring and Microservice Tracing in XRebel *
XRebel shines in distributed tracing by highlighting slow database queries across SQL and NoSQL databases. The tool traces requests end-to-end within their application UI, helping Java teams identify performance bottlenecks before code reaches production.

Real-time Feedback and Instrumentation Overhead in XRebel
XRebel features a lightweight, real-time, in-browser user interface that creates very low overhead, optimizing the experience specifically for development environments. Developers validate code changes instantly to fix Java performance issues faster and with less disruption.

IDE Integration in XRebel
XRebel supports the three most popular IDEs: IntelliJ IDEA, Eclipse, and VS Code. More than 42% of Java developers are using more than one IDE, with VS Code quickly rising in popularity for its flexibility and debugging capabilities. This integration allows teams to fit the tool seamlessly into their preferred development environments.

Head-to-Head Comparison: YourKit vs. JProfiler vs. XRebel

While YourKit, JProfiler, and XRebel are all purpose-built Java performance analysis tools, each has different capabilities and excel in different use cases. Read on to see how each tool stacks up.

Comparing Use Case, UI, and IDE Integration: How Each Tool Works
For starters, let’s take a closer look at the use case, UI, feedback models, IDE integration, and more for XRebel, JProfiler, and YourKit. While JProfiler and YourKit both have a full desktop UI, only XRebel offers a lightweight, real-time, in-browser UI. JProfiler and YourKit also don’t offer fully real-time performance analysis, although their capabilities differ here.

Each of the three Java performance tools discussed here support IntelliJ (by far the most popular Java IDE), but only XRebel supports VS Code, which is quickly gaining popularity for Java.

XRebel is also the only Java performance tool of the three that is specifically designed for development environments, whereas JProfiler can be used carefully in staging and production, and YourKit fully supports production profiling. XRebel also wins out for development use cases with low instrumentation overhead.

Profiling and Monitoring Capabilities: How Each Tool Solves Java Performance Problems
Now, take a closer look at which Java performance issues XRebel, JProfiler, and YourKit can help Java teams identify and track, and how each tool performs those functions.

XRebel works at the application level and provides only basic memory profiling capabilities, but it stands apart with live thread views, request profiling, and URL tracing.

XRebel wins with its ability to provide real-time, request-centric analysis. It continuously traces each request end-to-end: across web, service, and database layers. This automatically surfaces issues like slow queries or excessive calls as they happen, without requiring developers to start or interpret profiling sessions.

In contrast, JProfiler and YourKit provide method-level, session-based tracing that excels at deep diagnostics but requires more manual effort to connect insights across layers. XRebel simplifies this by presenting clear, actionable insights in context, helping developers understand why a request is slow during development rather than diagnosing issues after the fact.

When to Use XRebel Over JProfiler or YourKit
XRebel is simply the better choice if your Java team:

Is analyzing performance in development environments
Wants to work directly in their IDE instead of a separate desktop app
Needs real-time feedback to address Java performance issues

In these situations, YourKit and JProfiler simply don’t match the capabilities of XRebel.

When to Use JProfiler or YourKit Over XRebel
In some situations, XRebel is not the best option: e.g., production environments or situations where memory usage or garbage collection are a primary concern. Because XRebel focuses on the application level, it’s better suited for development Java performance analysis. YourKit, or in some cases, JProfiler, are the better fit for profiling Java code in production environments.

*Trial Information and Pricing: Putting Java Performance Tools to the Test *
The best Java performance analysis tool is the one your Java team will use — and the best way to determine that is with a trial during your next sprint. While YourKit, JProfiler and XRebel all offer commercial licenses and 10-15 day free trials, their pricing structures differ.

Final Thoughts on Java Performance Analysis

Finding performance bottlenecks early in the development cycle can save teams countless, debugging sessions trying to fix problems that have already hit production. That matters — because performance issues in production environments can wreak havoc and cause real consequences, including poor customer experiences, higher deployment costs, or even application downtime.

But the problems don’t just occur in production environments. Even Java performance issues caught in testing can still lead to unnecessary delays for code delivery.

That’s why addressing performance early in the development lifecycle can lead to much better (and less stressful) outcomes for your teams. With that in mind, the right tool for development stage Java performance optimization is clear.

Take a Proactive Approach to Java Performance With XRebel
By integrating XRebel into your Java development workflow, Java teams can catch performance issues before they begin to impact the business’s bottom line. This proactive strategy keeps your Java codebase clean, fast, and highly secure. DevOps engineers, performance engineers, QA engineers, systems engineers, developers, etc. stay focused on building innovative features rather than waiting for lengthy redeploys or dealing with complex production outages.

Want to see how XRebel can help your business overcome development bottlenecks to fix Java performance issues faster? Start your 14-day free trial today.

Rovo Agents Internals: How Scenarios, Triggers, and Skills Power Atlassian Automation

Dragonsoft DevSecOps — Fri, 24 Apr 2026 07:00:45 +0000

Summary:At the critical juncture where AI is evolving from "assisted Q&A" to "intelligent collaborator," Atlassian has officially launched Rovo Agents to provide IT and R&D teams with orchestrated, governable, and actionable automated workflows. In this article, Dragonsoft provides an in-depth interpretation of Atlassian's latest release, The Complete Guide to Rovo Agents, systematically breaking down the core architecture of Rovo Agents, design best practices, and pathways for DevOps scenario implementation — helping technical teams build highly reliable AI agents and achieve a substantial leap in R&D efficiency.

Atlassian's Guide to Rovo Agents:
Learn how to build, deploy, and scale your next AI teammates
A practical playbook with blueprints, best practices, and real‑world use cases for adding Rovo Agents into your workflows.

Why Rovo Agents?

Rovo agents are AI teammates that orchestrate work across all your business tools, connecting people, knowledge, and workflows wherever they live.

By automating repetitive tasks and surfacing the right context at the right time, they help teams move faster, stay focused on high‑value work, and deliver better outcomes — no matter which tools are in your stack.

Understanding Rovo Agents: The Building Blocks of Automation

Agents are AI‑driven systems that perform specific tasks by combining instructions, knowledge, skills, and triggers, then are augmented by AI reasoning. In Atlassian's ecosystem, Rovo agents act as orchestrators, automating workflows and delivering outcomes across products. They can be surfaced in chat, invoked in automation, or embedded directly into business processes.

At a high level, Rovo agents are like virtual teammates with a clear role, access to your organization's knowledge, and the ability to take actions on your behalf.

These agents run on top of a shared data layer called Atlassian's Teamwork Graph, which connects your organization's people, work, goals, and knowledge across Jira, Confluence, third‑party tools, and more. This lets them understand and reason over real organizational context, rather than treating each document as an isolated piece of information.

Rovo agents aren't limited only to Atlassian products; they connect to your entire ecosystem via a rich set of 50+ connectors, as well as virtually any application via the Model Context Protocol (MCP). You can automate workflows and orchestrate work across all your favorite tools. With Rovo, your AI teammates have access to the full picture, not just isolated silos.

The Core Components of a Rovo Agent

Every Rovo agent is built from a small set of building blocks:
INSTRUCTIONS | KNOWLEDGE | SKILLS | SCENARIOS AND TRIGGERS

Instructions
The "job description" and operating manual for the agent. Instructions define:
• The agent's role and goals (what it should and shouldn't do)
• The steps it should follow to complete tasks
• The tone and guardrails it should maintain (e.g., precise vs. exploratory)
• In Rovo Studio, this lives in the agent's behavior and scenario instructions.

Knowledge
The data and context that the agent can draw from. This can include:
• Third‑party tools connected to the Teamwork Graph (e.g., SharePoint, Google Drive)
• Atlassian sources like Jira projects and Confluence spaces
• Very specific business rules or templates referenced via Confluence Smart Links
• Knowledge can be broad (all of your Teamwork Graph) or tightly scoped to a domain (e.g., an IT helpdesk KB)

Skills (Actions and Plugins)
The tools the agent can use to read or change the work:
• Reading data: querying Jira issues, looking up customer records, pulling recent tickets
• Taking action: creating or updating Google Docs, Jira issues, drafting Confluence pages, posting to Slack, calling external APIs through Forge/MCP skills, sending a Microsoft Teams message, and sending Google emails
• Skills transform an agent from a passive answer bot into an active teammate that can move work forward

Scenarios and Triggers
To avoid a single, brittle prompt, agents can have multiple scenarios, each representing a specific task to be completed (e.g., "triage feedback," "draft release notes," "write a community post"). Each scenario has:
• Its own instructions, knowledge, and skills.
• A trigger that tells the agent when to use that scenario, based on user intent or event context.
• The default scenario is the fallback path when no other scenario's trigger is matched.

Combined with Rovo's admin and workspace settings, this lets teams deploy agents with clear guardrails; you decide which tools they can connect to, which scenarios are available to which users, and what level of autonomy each agent is granted.

These components are all coordinated by the underlying LLM, which acts as the "brain" of the agent, deciding how to interpret a request, which knowledge to search, which skills to invoke, and which scenario fits best.

Controls, Guardrails, and Governance
Scenarios and triggers also serve as powerful controls over when and how agents run. By tightly scoping each scenario's instructions, knowledge sources, and skills, you can:
• Limit what data the agent can access and which actions it's allowed to take
• Ensure sensitive workflows (such as HR or finance) run only under specific triggers or conditions
• Create separate "read‑only" and "action‑oriented" scenarios so you can phase in automation safely

What Happens When a User Sends a Prompt?

Whether a human types in chat or an automation rule invokes the agent, the flow is broadly the same:

I. Interpret the Request with AI and Context
The LLM reads the prompt, current conversation, and any structured inputs (e.g., issue fields from Jira). It identifies the user's intent and selects the most relevant scenario (or falls back to the default scenario).

II. Decide Which Knowledge and Skills to Use
Based on the scenario's configuration, the agent either:
i. Searches the appropriate knowledge sources (e.g., specific Confluence spaces, Jira projects, or other connected apps).
ii. Chooses which skills to utilize, such as researching related issues, retrieving customer history, or gathering recent feedback.

III. Reason, Orchestrate, and (Optionally) Take Action
The agent then:
i. Synthesizes information from multiple sources using the LLM.
ii. Applies the business rules encoded in its instructions (and any linked templates/rules pages).
iii. Optionally executes actions, such as:

Creating or updating Jira issues or JPD ideas.
Drafting or updating Confluence pages.
Sending notifications via Slack or other tools.

IV. Confirm and Summarize the Outcome
Finally, the agent:
i. Returns a clear, human‑readable summary of what it did and what it recommends next.
ii. Optionally outputs structured data (for example, JSON) so automations can branch on its decisions (e.g., only create new Jira Product Discovery ideas when a piece of feedback represents a genuinely new theme).

In practice, this means Rovo Agents can do far more than answer questions. They can:
• Eliminate repetitive or time‑consuming tasks, such as summarizing meeting notes or grouping hundreds of feedback tickets.
• Solve complex, multi‑step problems, like triaging customer feedback, comparing it to an existing roadmap, and only creating new ideas when needed.
• Improve decision‑making by pulling together context from across your Atlassian and third‑party tools, then presenting it in a way your teams can act on.

By combining instructions, knowledge, skills, and triggers on top of the Teamwork Graph, Rovo agents become the building blocks for reliable, end‑to‑end automation across Atlassian products and beyond.

When to Use Chat, Agents, or Both?

Chat
Chat is best when you need an immediate, ad‑hoc, human‑driven interaction. It's optimized for fast lookups, exploratory questions, clarifications, and one‑off decisions where a human will act on the answer.
• Ideal for quick searches, troubleshooting steps, brainstorming, or clarifying product/process rules on the fly.
• Good when the user expects conversational back‑and‑forth and manual follow‑up (no automatic side effects).
• Low setup cost: no need to configure knowledge scopes, skills, scenarios, or automations.

EXAMPLE:
Ask Rovo Chat, "How do I reset my VPN (Virtual Private Network)?" and instantly receive clear, step‑by‑step instructions or direct links to relevant assets in your knowledge base, so you don't have to search through documentation manually.

Rovo Chat understands common IT support questions and can interpret acronyms like VPN, automatically guiding you to the right troubleshooting steps or support resources.

Agents
Agents are best when work needs to be repeatable, deterministic, multi‑step, or integrated with other systems. They are designed to orchestrate tasks, call skills, and run on schedules or event triggers.
• Use when workflows are 2+ steps, require data aggregation, integration with tools, or must run automatically (scheduled or event‑driven).
• Use when you need structured outputs (e.g., JSON) so automations can branch, loop, and create artifacts deterministically.
• Use when you want a consistent tone/guardrails across multiple related tasks.

EXAMPLE:
By crafting a Rovo Agent to be a feedback triage sidekick, you'll have an AI assistant that continuously scans customer feedback channels (such as support tickets, NPS comments, and community forums), groups related comments into clear themes, checks the existing backlog in JPD (Jira Product Discovery), and automatically creates new idea work items when it finds unmet needs or gaps.

Instead of manually reading, tagging, and de‑duplicating feedback, you can get a prioritized, structured view of what customers are asking for, with direct links back to the original feedback and to the related ideas in Jira Product Discovery (JPD, Atlassian's product for capturing, prioritizing, and managing product ideas).

When to Use Both
There are many cases where Rovo Chat and Agent work together to be most powerful: start with chat for exploration and then transition the validated process to an agent when you need repeatability, automation, or integrations.
• Use chat to prototype prompts, gather examples, and validate the desired output format.
• Once validated, build an agent scenario that uses those instructions, binds knowledge sources, and attaches skills for automation.
• Example workflow: prototype "draft release notes" in chat → convert the prompt to an agent scenario with a trigger + Confluence/Jira skills → schedule it via an automation rule.

DECISION CHECKLIST - "Should I use Rovo Chat or an Agent?"

Is this a one‑time or occasional question? → CHAT
Is the task repeated often or at scale? → AGENT
Does it span multiple steps or systems (Jira, Confluence, third‑party APIs)? → AGENT
Do you need the output in a structured format (JSON) for downstream automation? → AGENT
Do you want it to run automatically on a schedule or event? → AGENT with Jira automations
Do you need exploratory back‑and‑forth or ad hoc human judgment? → CHAT (or hybrid: chat for exploration, agent for production)

If you find yourself repeating the same prompt or process, or if your workflow involves multiple steps, triggers, or integrations, building an agent is the best approach.

Key Considerations for Building Effective Agents

Before you start, clarify the problem you want to solve. Map out the workflow, identify the steps, and look for opportunities to automate or streamline. A focused problem statement and a clear picture of the current process make it much easier to design an agent that delivers reliable value rather than a vague "AI helper."
• Break down complex prompts into manageable scenarios for reliability.
• Define clear instructions, knowledge sources, and required skills for each scenario.
• Use behaviors to set the agent's tone and consistency across scenarios.
• Test and iterate

Finally, make it a point to collaborate with your team since agents can be long‑lived assets that benefit from shared ownership. They can be co‑owned and managed by groups, not just individuals, which means you can distribute responsibility for updates, monitoring, and incident response among team members.

Make sure to document how the agent works, assign clear owners, and encourage teams to contribute new scenarios, examples, and improvements so the agent continues to evolve alongside your workflows.

Best Practices for Agent Design and Prompting

Designing an effective agent starts long before you write the first instruction.

The most successful agents are treated like products: they have a clear purpose, a defined audience, and prompts that are written for reliability, not just creativity. In this section, we'll focus on how to shape your agent's "job description," how to write instructions that are unambiguous and repeatable, and how to use examples, templates, and structured outputs to get consistent results.

Think of these practices as your playbook for turning a powerful underlying model into a dependable virtual teammate that behaves the way your team expects, every time it's invoked.

Define Its Role and Objectives Clearly
Treat this like a job description: what problems is the agent responsible for, what is out of scope, and how will you measure success? A tightly scoped role makes it much easier to design prompts, choose the right data sources, and avoid unexpected behavior.

Write Instructions as If the Agent Has No Prior Context
Be explicit and step‑by‑step. Assume it's someone joining your team on their first day who has never seen your processes before. Spell out the sequence of actions, decision points, and any business rules you'd normally explain to a new hire. The more you remove ambiguity, the more predictable and reliable the agent becomes.

Provide Both Good and Bad Examples
This helps the agent have a framework that can deliver successfully more consistently. Use links to templates or business rules when possible. Just as you'd show a new teammate example tickets, documents, or past work, give the agent concrete samples of what "great" looks like, as well as what to avoid. Include links to Confluence pages, templates, or policy docs and instruct the agent to follow them. These examples serve as a pattern library that the agent can reference, thereby improving quality and consistency across responses.

Use Markdown or Tables for Structured Outputs
Structure is what turns a free‑form answer into something you can reuse and automate. Ask the agent to respond with headings, bullet lists, or fixed table columns so humans can scan quickly and downstream tools can parse results reliably. For more complex workflows, you can even specify simple schemas (for example, named sections or key‑value pairs) that the agent should always follow.

Iterate Based on Real‑World Testing and Feedback
Your first version is just a starting point, not the final product. Watch how people actually use the agent, capture failure cases, and refine the instructions, examples, or knowledge sources over time. Treat each iteration like coaching a teammate: give it clearer guidance, better samples, and updated rules as your processes evolve.

Conversation Starters and Scenario Triggers

While conversation starter examples can help users engage with agents, the focus should be on scenario triggers, which are clear, intent‑driven statements that guide the agent to the right scenario. Think of conversation starters as friendly entry points ("Ask the HR assistant about your benefits") and scenario triggers as the precise cues the agent uses to decide what to do ("Check my PTO balance for this quarter").

Well‑designed scenario triggers reduce ambiguity, improve routing accuracy, and make the agent's behavior more predictable.

Use positive and negative examples to refine triggers. Show the agent (and your team) what a "good" trigger looks like for each scenario, such as a specific, action‑oriented, and tied to a clear outcome, and contrast it with "bad" triggers that are too vague or misaligned.

EXAMPLE:
"Categorize this incoming ticket as incident, service request, or question" is a strong trigger, while "Help with ticket" is not. Over time, you can collect real user queries, label which ones should or shouldn't activate a scenario, and feed those back in as training examples to tighten the mapping.

Avoid using single‑word triggers; instead, provide context‑rich phrases that accurately reflect real user intent. A lone word like "report" or "access" can mean many different things, making it hard for the agent to select the right scenario reliably. Instead, aim for short, natural‑language phrases that encode both action and object, such as "Generate a weekly incident report for my team" or "Request access to the marketing dashboard."

These richer triggers make it easier for the agent to distinguish between similar workflows, reduce misfires, and ensure users quickly land in the experience that actually solves their problem.

Explore Practical Use Cases

Rovo agents are already powering a wide range of workflows, from engineering to HR to product management. They shine wherever work is repetitive, multi‑step, or requires pulling context from multiple tools and turning it into clear, actionable outcomes.

Featured examples include:

Issue Organizer
Automatically review issues in a backlog, group related work, move issues into the right sprints, and assign them to the correct epics or owners. This helps teams keep boards clean and focused without the need for hours of manual grooming.

Release Notes Drafter
Pull details from Jira issues, such as summaries, labels, and fix versions, and turn them into clear, user‑friendly release notes. The agent can propose a first draft that your team can quickly review, edit, and publish to Confluence or share with stakeholders.

Employee Onboarding
Generate tailored onboarding plans that combine HR policies, role‑specific documentation, and team rituals. The agent can assemble checklists, reading lists, and a week‑by‑week plan to help new hires ramp up faster and more consistently.
These are just starting points: once you understand how each use case combines instructions, knowledge, and skills, you can remix them into agents that fit your team's unique workflows.

Craft the Next Great Rovo Agent

Ready to go from zero to hero with Rovo? Start by combining a short, focused learning loop with a small, high‑value build. Learn the core concepts, follow one end‑to‑end example, then ship a simple agent and iterate based on real usage.

Quick Start Project: Build Your First Agent
Build a single, high‑impact agent in 4 steps. Each step is small enough to finish in a day or two.

1: Write a Problem Statement
Write a one‑paragraph problem statement that clearly identifies the user and the specific outcome.

2: Draft a Simple Scenario
Draft a simple scenario with 3–4 behaviors (greet, gather inputs, perform action, confirm).

3: Implement and Test
Implement the scenario in Rovo Studio and run 5 internal tests with colleagues.

4: Release and Iterate
Release to a small pilot group, collect feedback, and iterate.

Begin with a narrowly scoped use case (for example, triaging incoming tickets, summarizing meeting notes, or automating a routine approval).

Narrow scope = faster learning and measurable impact.

Best Practices to Follow
Start simple and measure early
• Keep the first agent to a single primary goal.
• Add telemetry to track usage, success rate, and provide a brief feedback message after each session.
• Use versioned changes so you can roll back quickly.
• Collect one qualitative user quote per week during the pilot to guide prioritization.

Suggested 30/60/90 Day Plan

0–30 Days:
Complete the fundamentals course, watch the video, and post in the hub. Draft your problem statement and scenario.

31–60 Days:
Build the agent in Studio, run tests, and launch a 5–10 user pilot. Collect quantitative and qualitative feedback.

61–90 Days:
Iterate based on feedback, add one additional behavior or knowledge source, and prepare a team rollout plan.

Rovo agents unlock scalable automation when you pair learning with small, real projects. Select one clear problem, follow the steps above, and begin building today to deliver measurable value to your team.

Try Rovo Today

Even though Rovo is still early in its journey, the pattern is already clear:
teams that treat agents like real teammates with clear roles, strong instructions, and tight loops between learning and building see the fastest and most durable impact.

Start small, pick one high‑value workflow, and use the practices in this guide to design an agent that your team can trust and evolve over time.

Translating AI exploration into tangible R&D efficiency relies on rigorous architectural design and scenario accumulation. As an Atlassian Global Platinum Partner, Longzhi provides end-to-end services covering Rovo Agents planning, DevOps scenario customization, cross-toolchain integration, and Agent governance. To obtain a customized AI workflow implementation plan or schedule a demo with our technical architects, contact us today.

Connect SonarQube to Cursor & Claude: Real-time Context for Better AI Code

Dragonsoft DevSecOps — Thu, 23 Apr 2026 03:25:08 +0000

While AI coding agents like Cursor and Claude Code have drastically accelerated the development process, they often operate in a vacuum—lacking the specific architectural boundaries, security standards, and "tribal knowledge" of your unique codebase. This often leads to "AI hallucinations" and costly trial-and-error cycles.
Sonar Context Augmentation is here to change the game. By leveraging the Model Context Protocol (MCP), it injects real-time, verified, and structured insights from SonarQube directly into the AI agent’s reasoning engine. As an authorized partner of Sonar, Dragonsoft is excited to deep dive into this milestone feature, which empowers your team to embrace the Agent-Centric Development Cycle (AC/DC) without sacrificing code quality or security.

On March 3rd, 2026, Sonar launched the closed beta of Sonar Context Augmentation for Enterprise customers. And March 31st, Sonar officially Sonar announced the open beta—expanding access to all SonarQube Cloud Teams and Enterprise plan customers.

Overview

Sonar Context Augmentation enhances AI-assisted code analysis by providing structured, verified context from SonarQube findings to AI agents, giving them accurate, up-to-date information about code quality and security issues.
Rather than relying on LLMs to independently analyze code—where hallucinations and outdated knowledge create risk—Context Augmentation grounds AI responses in Sonar's deterministic analysis output.
This capability enables agentic AI workflows to prioritize and reason about code issues more accurately, improving the quality of AI-generated remediation suggestions and code reviews.
By connecting Sonar's analysis engine to AI agents via MCP (Model Context Protocol), teams can build AI-powered workflows that leverage verified security intelligence without sacrificing accuracy.

AI coding agents like Cursor and Claude Code are changing how we build software, but they often work in a vacuum. They don’t automatically understand your project’s specific rules, architectural boundaries, or code security standards. As a result, they can generate code that works in isolation but fails to integrate cleanly into your broader codebase. This leads to rework, higher costs, and a "trial-and-error" process for software developers.

To address this, Sonar introduced the Agent Centric Development Cycle (AC/DC), a framework built for the age of AI, with four continuous stages: Guide → Generate → Verify → Solve. Sonar announces the beta of Sonar Context Augmentation to help agents in the Guide stage of AC/DC.

What is Sonar Context Augmentation?

Sonar Context Augmentation is a service that injects real-time, deep knowledge from SonarQube directly into your AI agent’s workflow. It uses the SonarQube MCP Server to act as a bridge between your enterprise codebase and your AI coding tools.

By providing this "repo-aware" context, Context Augmentation helps AI coding agents understand the specific environment they are working in before they ever generate a line of code.

How Sonar Context Augmentation works

Sonar Context Augmentation provides the exact, filtered information an AI agent needs to get the job right on the first try:

Dynamic context guidelines: Instead of overwhelming an agent with thousands of rules, Context Augmentation identifies the most relevant coding standards based on what you are asking and the history of the specific files being modified.
Architectural awareness: It gives the agent a structural map of your code, including class hierarchies and function flows, so it respects your intended code architecture and avoids creating technical debt.
Smarter search: Agents can find specific code sections using exact signatures and body patterns rather than simple text matches, leading to more accurate modifications.

Why context matters

When an agent has the right context, the agentic output is more accurate, faster, and carries less risk for long-term architectural drift. Experience increased build pass rates, increased test pass rates, significantly reduced code duplication, and reduced cognitive complexity. All of this matters for achieving the real productivity promise of AIgen code.

Sonar benchmarking also shows that when an agent has the right context, it doesn't just write better code—it works more efficiently. Organizations using Context Augmentation can expect to see reduction in token usage, tool calls, and overall AI operating costs, in particular when working in complex code bases.

By defining the "rules of engagement" upfront, developers spend less time fixing AI-generated code errors and more time shipping high-quality software.

Try it out during our beta

The Sonar Context Augmentation beta is available starting today. To participate, you will need:

SonarQube Cloud Team or Enterprise annual or monthly plan
SonarQube MCP Server
An AI agent like Cursor, GitHub Copilot or Claude Code
Any language project to leverage intelligent guidelines
A Java, C#, Python, JavaScript or TypeScript project to leverage
architectural awareness (intended architecture must be set configured for the project to leverage your intended architecture)

NOTE: Context Augmentation only supports projects using CI-based analysis. Projects using Automatic analysis in SonarQube Cloud will not work with Context Augmentation.

For detailed steps to set up Sonar Context Augmentation see Sonar’s documentation. We hope you will try it out during the beta and explore how agents like Cursor and Claude Code can follow your organization’s specific standards from the first prompt.

Don't Just Generate Code—Engineer It with Precision.

In the era of Generative AI, the difference between "working code" and "enterprise-grade code" lies in the context. Dragonsoft is committed to providing world-class DevSecOps solutions and expertise to help you navigate the complexities of modern software engineering.

Ready to eliminate AI hallucinations and boost your team's productivity with Sonar Context Augmentation?

Whether you need a technical deep dive, a customized MCP setup, or an enterprise-wide AI strategy, our experts at Dragonsoft are here to help.

Stop waiting for Java to rebuild! AI IDEs + Zero-Latency Hot Reload = Magic

Dragonsoft DevSecOps — Thu, 09 Apr 2026 08:49:54 +0000

The way developers write code has fundamentally shifted. AI development tools are accelerating the DevOps feedback loop, and AI-native IDEs — i.e., IDEs built with AI at their core — are driving that change. They are fast becoming the standard for development teams that want to ship faster, reduce context-switching, and keep pace with increasingly complex Java codebases.

But not all AI IDEs are the same when it comes to coding in Java. Cursor, Windsurf, AWS Kiro, and Google Antigravity each take a different philosophical approach to how AI and developers collaborate. Each one excels at a different area, e.g., multi-agent coding or spec-driven development. Read on for a closer look at the unique use cases for coding in Java with each.

What Is an AI-Native IDE?

A traditional IDE assists developers with syntax highlighting, code completion, and debugging. An AI-native IDE goes further: it embeds AI directly into the development workflow, capable of understanding your codebase, writing full functions, refactoring code, catching bugs, and even planning architectural changes.

The defining characteristic of AI-native IDEs is that their agentic features are not bolted on or plugins. Moreover, all four AI-native IDEs discussed in this blog post — Cursor, Windsurf, Kiro, and Antigravity — are forks of VS Code, i.e., an independent code editor created by copying and editing the open-source VS Code codebase. Marketplaces like OpenVSX further leverage the power of the open source community with hundreds extensions.

Dive into the differences between AI plugins and AI-native IDEs. Read the blog.

Who’s Using AI IDEs and How Are They Being Used for Java Development?

AI IDEs are in a hyper-growth phase. Some Java development teams are using AI-native IDEs to generate code, then committing to that code in a traditional IDE (like IntelliJ IDEA), while others are further along on the adoption curve and use AI-native IDEs for all aspects of their Java development process.

Similarly, adoption rates for each of the four covered AI-native IDEs runs the gamut from mainstream to edge use cases:

Cursor is by far the most popular AI-native IDE with more than 1 million downloads and 300,000 active users to date.
Windsurf boasts more than 500,000 active users, with 50,000+ developers using the AI code generator on a weekly basis.
Antigravity is growing, especially within the Google ecosystem.
Kiro is relatively new, with growing adoption among AWS customers.

Cursor: The AI Pair Programmer

Cursor positions itself as an AI pair programming tool, keeping the human developer firmly in the loop. For instance, bug fixes are applied via agent workflow, giving developers direct control over when and how AI suggestions are applied.

Context management is moderate compared to competitors, and cross-session memory is limited to the current chat. Multi-agent mode is partially supported. Cursor supports code and documentation, with partial support for planning workflows.

Cursor supports multiple AI models and integrates well with standard development environments. However, it does not offer browser integration or native UI testing capabilities, which may limit its appeal for full-stack workflows.

Adoption and Pricing of Cursor for Java

Cursor has established a strong foothold across development communities, with more than 1 million downloads and around 300,000 active users to date. Its pricing model appeals to individual developers and enterprise developers alike.

Pricing:

Hobby: Free
Pro: $20/month
Pro+: $60/month
Ultra: $200/month
Team and enterprise plans are also available

Windsurf: The AI Teammate

Windsurf frames AI as a teammate rather than a tool, making it particularly well-suited for large codebases. Its flagship feature, Cascade, combines deep codebase understanding with real-time awareness of developer actions. Cascade can automatically detect and fix lint errors, track previous actions, and continue work across sessions — all without losing context.

Windsurf offers persistent, large-scale memory across sessions, which is a meaningful upgrade over tools with per-chat context limits. Image-to-code support (via drag-and-drop into Cascade) also adds a useful multimodal dimension to the workflow.

Windsurf supports MCP (Model Context Protocol) natively, connecting to tools like Figma, Slack, Stripe, GitHub, and PostgreSQL.

What sets Windsurf apart is its in-house SWE1.5 model, which works alongside other AI models such as Claude, GPT, and BYOK. Users can select their AI model based on capabilities and cost.

Adoption and Pricing of Windsurf

Windsurf has more than 500,000 active users and more than 4,000 enterprise customers. It is positioned for both individual developers and enterprise teams.

Pricing:

Free: $0
Pro: $15/month
Pro (per user/month): $30
Enterprise plans available

AWS Kiro: Spec-Driven Development

Kiro is built on spec-driven development, a fundamentally different approach from Cursor, Windsurf, and Antigravity. Rather than jumping straight into code generation, Kiro first translates natural language prompts into structured requirements using EARS (Easy Approach to Requirements Syntax) notation. From there, the agent produces an architectural design and an implementation plan with discrete, sequenced tasks.

This structured workflow makes Kiro particularly powerful for larger, more complex projects. Multi-agent mode is fully supported, and Kiro offers strong context management across large codebases. Memory operates per-agent session rather than persisting across the entire project history.

Kiro's planning capabilities are comprehensive, and its terminal interaction is workflow-based. Agent hooks—triggered by events such as file saves—allow Kiro to autonomously generate documentation, run unit tests, or optimize code in the background.

Adoption and Pricing of AWS Kiro

Kiro is based on Code OSS and supports Open VSX plugins, themes, and VS Code settings, making migration from VS Code relatively straightforward. Kiro is relatively new and growing in popularity, especially among AWS users.

Pricing:

Individual: Free (with generous weekly rate limits)
Developer: $20/month
Team plans available

Google Antigravity: Multi-Agent Development

Google Antigravity sets itself apart, with multiple agents running in parallel across your editor, terminal, and browser simultaneously. Its Agent Manager allows developers to orchestrate several agents across different workspaces from a single mission control view.

Antigravity's editor offers tab autocomplete, natural language code commands, and a configurable, context-aware agent. Higher-level abstractions present developers with verification results and essential artifacts, building trust in what the agent produces.

A standout feature is full browser integration, including UI testing through its "nano banana" model. This positions Antigravity as the most capable option for teams building and testing front-end applications, or anyone who wants agentic control that extends beyond the code editor.

The platform supports Gemini models natively, alongside other frontier models. Diagram-to-code functionality adds another multimodal capability that goes beyond simple image imports.

Adoption and Pricing of Google Antigravity

Antigravity usage is currently growing within the Google ecosystem. As a newer entrant, its user base is still expanding.

Pricing:

Individual: Free (no charge)
Team plans: Coming soon

Key Considerations for Choosing an AI-Native IDE

No single AI-native wins across every dimension. The right choice depends on your team's specific workflow, codebase size, and development philosophy.

This chart compares the key differences in philosophy, AI models, developer workflow model, terminal interaction, browser integration, UI Testing, etc. between Cursor, Windsurf, Kiro, and Antigravity.

Which AI Native IDE Is Best for Java Development?

There is no one right IDE for Java development, but each AI-native IDE shines in a particular area.

Choose Cursor if you value tight human-AI collaboration and an excellent autocomplete experience for individual work.
Choose Windsurf if you need a persistent AI teammate with strong memory and large codebase support, particularly for enterprise teams.
Choose Kiro if your team values structured, documented development workflows and needs an AI that turns prompts into requirements before writing a single line of code.
Choose Antigravity if you want full agent-first development across editor, terminal, and browser, especially if UI testing is part of your workflow.

What’s Next in AI-Native IDEs

Cursor, Windsurf, Kiro, and Antigravity are not the only AI IDEs for Java on the market today. Discover how Jetbrains Air and Claude Code could upset these rankings as they mature.

JetBrains Air
One platform worth watching is JetBrains Air. Given JetBrains' deep roots in Java development and the popularity of IntelliJ, JetBrains Air has the potential to resonate strongly with Java-focused teams. For developers already invested in the JetBrains ecosystem, it may offer the most seamless AI-native transition when it becomes generally available.

Claude Code
Claude Code is a terminal-native AI assistant that integrates with your existing code editors and tools. Unlike other AI IDEs, Claude Code prioritizes transparency, control, and a deep understanding of the codebase.
Among other capabilities, Claude Code can map and explain your entire codebase in a few seconds. Claude moved to general availability in May 2025 alongside the announcement of Claude 4. Look for Claude to gain popularity as an AI IDE for Java as developers begin to prioritize using AI without context switching.

Final Thoughts on the Best AI IDEs for Java Coding

AI-native IDEs accelerate how fast developers can write and refactor code. But for Java developers, there is another friction point that consistently impacts their productivity: redeploy times. Every time you make a change and wait for a rebuild and redeploy cycle, you lose your development flow.
JRebel eliminates that wait entirely. By allowing Java developers to see code changes instantly while maintaining application state, JRebel keeps you in the flow that AI-native IDEs work so hard to create.

Supercharge AI & Eliminate Deployment Delays! — Reshape Your Java Flow with Dragonsoft
AI-native IDEs (Cursor, Windsurf, etc.) push coding speeds to the limit. But lengthy builds and app restarts remain a glaring bottleneck, killing your momentum and wasting AI's efficiency gains.
True development speed requires zero-latency feedback.

The Dragonsoft Solution: AI-Native IDE + JRebel = Ultimate Efficiency
Say goodbye to "code for 1 minute, wait 5 minutes." JRebel hot-reloads Java code instantly without rebuilding, letting you perfectly keep pace with AI's rapid output!

Partner with Dragonsoft to Multiply Your Efficiency:

JRebel Trial: Eliminate restart waits and maximize your AI time savings.
Expert Consulting: Tailored AI tool selection and seamless integration.
End-to-End Toolchain: From smart coding to secure delivery, we build your next-gen DevSecOps pipeline.

Don't let slow compiles hold back your AI!
Contact Dragonsoft today for your JRebel trial and AI implementation guide, and keep your Java team in the perfect coding flow!

Accelerating AI-Driven Workflows: How the Atlassian and AWS Partnership Redefines Productivity

Dragonsoft DevSecOps — Thu, 09 Apr 2026 08:08:37 +0000

Abstract: Cloud migration is more than a tech upgrade—it's an AI enabler. As an Atlassian Global Platinum Solution Partner, Dragonsoft explores how the Atlassian-AWS synergy and the Cloud Center of Excellence (CCoE) are streamlining complex migrations. Discover how this partnership helps organizations achieve a 55% boost in scalability while Dragonsoft ensures a seamless transition for your global teams.

Cloud is the key to enterprise transformation

The transition to cloud computing is more than just a technological upgrade; it allows organizations to establish a robust foundation that significantly improves their agility, scalability, and collaborative potential. Organizations are rapidly embracing cloud environments not only to modernize their IT infrastructures but also to achieve transformative business outcomes.

Key Benefits of Cloud Technologies:

Accelerated access to sophisticated AI and enhanced security features.
Enhanced collaboration capabilities among diverse teams.
Amplified analytics capabilities for data-driven decision making.
Increased productivity and innovation throughout organizations.
Utilization of AWS services to develop applications on Atlassian’s cloud platform.

Adopting cloud technologies fosters a culture of enhanced collaboration, expedites decision-making processes, and empowers enterprises to use the full capabilities of analytics. Additionally, it allows organizations to use intelligent automation and integrate workflows, collectively contributing to a more efficient and responsive organizational structure. This better positions them to navigate the complexities of the modern business environment and sustain competitive advantage.

52%of surveyed executives cite AI as the leading reason to move to cloud.
-2025 Executive Insights Report

Why this partnership matters

Since 2011, Atlassian and AWS have collaborated to deliver scalable, secure, and innovative solutions tailored to enterprise needs. This partnership empowers organizations to deploy faster and scale collaborative work experiences, while minimizing their infrastructure investment.

To support large-scale migrations, Atlassian and AWS have established a Cloud Center of Excellence (CCoE) that streamlines complex migrations, reducing migration time. It also equips specialized solution providers with cloud and AI expertise to resell AWS-powered Atlassian Cloud offerings and assist millions of users in migrating to the cloud.

Through collaboration with AWS and Atlassian, enterprise customers can realize cloud benefits faster

Organizations that have already moved are seeing greater scalability (55%), reliability (39%), agility (36%), and cost reduction (31%).
-2025 Executive Insights Report

The primary advantages of this partnership are the acceleration of organizational innovation and enhancement of user experience. Leveraging the combined strengths of Atlassian and AWS allows
organizations to streamline their workflows and improve overall user satisfaction.

Furthermore, it allows organizations to explore novel work methodologies that can significantly enhance their productivity. By consolidating tools through a diverse set of interconnected applications, teams can identify more efficient approaches to achieving their objectives.

The partnership also lets enterprises leverage collective expertise, facilitating the exploration of new business opportunities through use of AWS AI and analytics services. The ability to share insights and resources allows organizations to access new markets and strengthen their competitive position.

Finally, the partnership helps organisations to simplify security, infrastructure, and integration challenges in product development. This allows them to focus on innovation without being hindered by complex security and infrastructure issues, to streamline the
development process.

Migration Capabilities

Facilitating smooth and secure large-scale migrations not only accelerates organizational innovation but also enables teams to discover more effective ways of working. Secure and controlled migration also helps organisations to lay the groundwork for a more adaptable, scalable and future-proofed technology foundation, ensuring readiness to meet the demands of a fast evolving technological landscape.

By working closely with specialized partners, and providing joint migration support, the goal is a seamless migration to help organizations improve operational efficiency and foster an environment that allows creativity and productivity to flourish.

Let’s supercharge the future of work

The strategic partnership between Atlassian and AWS offers a significant opportunity to drive cloud transformation and deliver advanced AI capabilities to enterprise customers, supported by AWS world-class infrastructure, security, and privacy.

Together, we empower customers with cutting-edge cloud solutions, that enhance productivity and innovation throughout every inch of their organizations, allowing their teams to transform ideas into products using the most comprehensive and widely adopted cloud infrastructure available.

About Atlassian.
Atlassian unleashes the potential of every team. A recognized leader in software development, work management, and enterprise service management software, Atlassian enables enterprises to connect their business and technology teams with an AI-powered system of work that unlocks productivity at scale. Atlassian’s collaboration software powers over 80% of the Fortune 500 and 300,000+ customers worldwide - including NASA, Rivian, Deutsche Bank, United Airlines, and Bosch - who rely on Atlassian solutions to drive work forward.

About Amazon Web Services
Since 2006, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud. AWS has been continually expanding its services to support virtually any workload, and it now has more than 240 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, media, and application development, deployment, and management from 120 Availability Zones within 38 Geographic Regions, with announced plans for 10 more Availability Zones and 3 more AWS Regions in the Kingdom of Saudi Arabia, Chile, and the AWS European Sovereign Cloud. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com.

Ready to embark on a seamless migration journey? Click here to contact Dragonsoft, an Atlassian Global Platinum Solution Partner, for a free assessment today.

IntelliJ IDEA 2026.1 is Here: Terminal Upgrades & 1000+ Bug Fixes!

Dragonsoft DevSecOps — Thu, 09 Apr 2026 07:52:08 +0000

Hey devs!
As a JetBrains partner, we at Dragonsoft are excited to share what’s packed into the new IntelliJ IDEA 2026.1 release.
If you've ever dealt with annoying UI freezes or terminal focus issues, this update is for you. JetBrains crushed over 1,000 bugs!
Here is a quick breakdown of what they changed:

Performance

JetBrains continues to prioritize reliability, working to improve application performance, fix freezes, optimize operations, and cover the most common use cases with metrics. Using their internal tools, the development team identified and resolved 40 specific scenarios that caused UI freezes.

However, internal tooling alone cannot uncover every issue. To identify additional cases, JetBrains enabled automatic error and freeze reporting in EAP builds. By collecting this data, JetBrains gains a real, unfiltered picture of what’s going wrong, how often it happens, and how many users are affected. This allows JetBrains to prioritize fixes based on real impact rather than guesswork.

As always, JetBrains prioritize your privacy and security. When using EAP builds, you maintain full control and can disable automatic error and freeze reporting in Settings | Appearance & Behavior | System Settings | Data Sharing.

Terminal

Version 2026.1 enhances your productivity by streamlining the experience offered by the terminal, a crucial workspace for developer workflows involving CLI-based AI agents.

First, this release fixed the Esc behavior – it is now handled by the shell instead of switching focus to the editor, so it does not break the AI-agent workflow. Additionally, Shift+Enter now inserts a new line, making it easier to write multi-line prompts and commands directly. This behavior can be disabled in Settings | Advanced Settings | Terminal

The update also improved the detection of absolute and relative file paths in terminal output, allowing you to open files and folders with a single click in any context. When you encounter compilation or build errors, or submit a task to an AI coding agent, you can jump directly to the referenced file and review or fix issues faster.

Link navigation is activated by holding Ctrl (or Cmd on macOS) and clicking – just like in external terminals.

JVM language support

Better Kotlin bean registration support
Kotlin’s strong DSL capabilities are a perfect fit for Spring Framework 7’s BeanRegistrar API. In 2026.1, JetBrains has made working with programmatic registration as productive as annotation-based configuration.

The IDE ensures complete visibility into your application structure thanks to the Structure tool window, providing better endpoint visibility, intuitive navigation with gutter icons, integrated HTTP request generation, and path variable support.

New Kotlin coroutine inspections
To help maintain code quality, the new release has introduced a set of new inspections for the Kotlin coroutines library, covering common pitfalls.

Scala
Working with sbt projects inside WSL and Docker containers is now as smooth as working with local projects. They have also improved code highlighting performance and sped up sbt project synchronization.

To reduce cognitive load and provide a more ergonomic UI, the new release has redesigned the Scala code highlighting settings. A new Settings page consolidates previously scattered options, making them cleaner, more intuitive, and easier to access.

You can now disable built-in inspections when compiler highlighting is sufficient, or configure compilation delay for compiler-based highlighting. Settings for Scala 2 and Scala 3 projects are now independent, and the type-aware highlighting option has been integrated with the rest of the settings.

Spring

Spring support remains a core focus for IntelliJ IDEA. JetBrains is committed to maximizing reliability and reducing friction in your daily development.

This release made a dedicated effort to address issues related to running Spring Boot application from the IDE. There are now even fewer reasons to run your application in the terminal – just run it in the IDE and use the debugger when you need deeper insights.

Spring Boot 4 API versioning support
This is a new Spring Boot feature, and JetBrains keeps improving its support based on your feedback. In this version, JetBrains added .yml files support for version configuration, fixed false positives and added a couple of useful inspections, so you get an instant feedback about issues without running the app.

Flyway DB Migrations
To ensure a reliable and distraction-free experience, the IDE now verifies migration scripts only when a data source is active, eliminating false-positive errors when the data source is disconnected.

At the same time, Flyway scripts got correct navigation to the table definitions, and SQL autocompletion for any files and tables defined in them.

User interface

With IntelliJ IDEA 2026.1, JetBrains has continued to prioritize ultimate comfort and an ergonomic UI, ensuring your workspace is as accessible and customizable as your code.

The long-awaited ability to sync the IDE theme with the OS is now available to Linux users, bringing parity with macOS and Windows. Enable it in Settings |Appearance & Behavior | Appearance.

The code editor now supports OpenType stylistic sets. Enjoy more expressive typography with your favorite fonts while coding. Configure them via Editor |Font, and preview glyph changes instantly with a helpful tooltip before applying a set.

Windows users who rely on the keyboard can now bring the IDE’s main menu into focus by pressing the Alt key. This change improves accessibility for screen reader users.

Version control

JetBrains continues to make small but impactful improvements that reduce friction and support your everyday workflow.

You can now [coming in 2026.1.1 update] amend any recent commit directly from the Commit tool window – no more ceremonies involving interactive rebase. Simply select the target commit and the necessary changes, then confirm them – the IDE will take care of the rest.

In addition to Git worktrees, the IDE has improved branch workflows by introducing the Checkout & Update action, which pulls all remote changes.

Furthermore, fetching changes can now be automated – no need for a separate plugin. Enable Fetch remote changes automatically in Settings | Git.

In-IDE reviews for GitLab merge requests now offer near feature parity with the web interface. Multi-line comments, comment navigation, image uploads, and assignee selection when creating a merge request are all available directly in the IDE, so you can stay focused without switching to the browser.

The Subversion, Mercurial, and Perforce plugins are no longer bundled with the IDE distribution, but you can still install them from JetBrains Marketplace.

Databases

JetBrains has enhanced the Explain Plan workflow with UI optimizations for the Query Plan tab, an additional separate pane for details about the execution plan row, inner tabs that hold flame graphs, and an action to copy the query plan in the database’s native format.

JetBrains daemon

IntelliJ IDEA 2026.1 includes a lightweight background service – jetbrainsd – that handles jetbrains:// protocol links from documentation, learning resources, and external tools, opening them directly in your IDE without requiring you to have the Toolbox App running.

Sunsetting of Code With Me

As of version 2026.1, Code With Me will be unbundled from all JetBrains IDEs and will instead be available as a separate plugin on JetBrains Marketplace. Version 2026.1 will be the last IDE release to officially support Code With Me as JetBrains gradually sunset the service.

Enhanced AI management and analytics for organizations

JetBrains is working hard to provide development teams with centralized control over AI and built-in analytics to understand adoption, usage, and cost. As part of the effort, JetBrains has introduced the JetBrains Console. It adds visibility into how your teams use AI in practice, including information about active users, credit consumption, and acceptance rates for AI-generated code.

The JetBrains Console is available to all organizations with a JetBrains AI subscription, providing the trust and visibility required to manage professional-grade development at any scale.

What’s your favorite new feature? Let us know in the comments! (And hit up Dragonsoft if your team needs enterprise licenses!)

Stop Managing Multiple Dashboards: Unifying IT and Enterprise Services with HaloITSM

Dragonsoft DevSecOps — Fri, 03 Apr 2026 04:58:51 +0000

As digital transformation accelerates, internal service management within enterprises often suffers from a severe "silo effect": the IT department uses one system, HR relies on another, and Facilities has its own distinct workflows. Operating multiple platforms in parallel not only inflates operational costs but also significantly degrades the end-user experience.

How can organizations break down these departmental barriers and achieve seamless Enterprise Service Management (ESM)? As an expert dedicated to providing one-stop DevSecOps and IT management solutions, DragonSoft has partnered with HaloITSM—an industry-leading platform backed by over 25 years of proven expertise—to provide the perfect answer.

HaloITSM: A fully functional modern service management platform

HaloITSM is a privately owned and run company.With its development starting in 1994, it has been dedicated to providing premier IT Service Management (ITSM) and Enterprise Service Management (ESM) software solutions.

Unlike many competitors, HaloITSM is all-inclusive. This means that there are no hidden costs for additional features, making it a cost effective solution.

Furthermore, being highly configurable and with low effort required, experience ITSM aligned to your needs.
HaloITSM’s all-inclusive nature means all modules are included as standard. This means you won’t be charged extra should you wish to take a phased approach to utilising modules.You can simply configure additional service areas and add in new team as you deem necessary.

Many clients make use of HaloITSM as an Enterprise Service Management (ESM) solution, allowing departments other than IT, such as HR and Facilities, to also enjoy the benefits of HaloITSM.

5 Reasons to Choose HaloITSM

With HaloITSM, not only do you receive a best of breed ITSM solution, but also a great experience. HaloITSM likes to think of themselves, not as a vendor, but as a partner.
HaloITSM is trusted by global organizations including Microsoft, Siemens, and Suzuki and has received numerous awards such as PCMag's Editors' Choice award.

1. Modern & Intuitive UI
Move away from old and clunky systems. HaloITSM is an easy to use solution, which requires minimal effort to get up and running.

2.Highly & Easily Configurable
Configure HaloITSM according to your organisation's needs in just a few clicks.

3.Responsive Support Team
With Dragonsoft techincal support team and HaloITSM in-house support available around the clock, you’ll enjoy a more responsive support system to put your mind at ease.

4.Futureproof System
Experience an ITSM solution that you can grow with. Stay up to date on new features and functionalities with our publicly available roadmap.

5.Lower Costs
Reduce the cost of your service delivery with HaloITSM. Our pricing is simple and transparent: no tiered-plans or locked-away features, just all-inclusive ITSM.

Testimonial

"The good thing with Halo is how the tool can be customized to suit our requirements specific to SKY's business environments. This is a much better state to be in rather than the tool asking the business to adapt to make it work."
— Vamsi Dharanikota, Service Delivery Manager - SKY

From ITSM to ESM: Transforming Enterprise Services with a Unified Platform

HaloITSM’s value isn't confined to IT alone. More and more organizations are using it as the backbone of their Enterprise Service Management (ESM) strategy, expanding automated workflows into HR, Facilities, Finance, and other business units to create seamless, standardized processes. Unifying all departments under a single HaloITSM platform empowers you with the following key benefits:

Centralisation of Data
Access and view data from every department in one central system.
One Portal for End-Users
Let users easily find and request the exact services they need from one central portal.
Standardisation of Processes
Streamline complex processes into simple flows with HaloITSM's workflow engine.
Fewer Systems to Administrate
Gone are the days of having different tools and protocols for different departments.
Lower Software Costs
Decrease software spend across your organisation with one annual software spend and a decreasing per user cost with every license added.
Scalable Growth
HaloITSM's Enterprise Service Management solution is designed to let you grow with the system - add new teams to Halo as you see fit.
Improve Interdepartmental Communications
Let users easily find and request the exact services they need from one central portal.

Customer Success Story - PSPS Ltd

PSPS have been using HaloITSM since 2019 and have since extended their workflows beyond IT by using it as an Enterprise Service Management solution. PSPS now experience the benefits of HaloITSM throughout their entire organisation, including the following:

Unlock Productivity：Save hours of manual admin work every day
Drive Data-Driven Decisions: Improved efficiency and attained complete transparency over operations
Accelerate Service Response: Enhanced communication within and between departments

Embark on Your Service Management Transformation with DragonSoft

In today's fiercely competitive digital landscape, choosing a flexible, cost-effective, and future-proof ITSM platform is a critical decision for boosting operational efficiency and optimizing the enterprise-wide service experience.

As a key partner of HaloITSM, DragonSoft leverages deep expertise in DevSecOps and Enterprise Service Management (ESM) to provide you with end-to-end escort services—from requirement analysis and tailored solution design to system implementation and localized technical support. We help you break down internal service silos, ensuring that you acquire not just a powerful tool, but a trusted, long-term technical advisor on your digital transformation journey.

Say goodbye to fragmented systems. Take the first step today:

Request a free trial and get hands-on experience with HaloITSM's modern, intuitive platform.
Book a Personalized Demo: Let DragonSoft's technical experts walk you through custom ESM workflows tailored to your specific pain points across IT, HR, and Facilities.
Get Your Transformation Roadmap: Receive an in-depth assessment of your current toolchain and a customized upgrade path.

Contact DragonSoft today and enable seamless collaboration between your IT and business departments!

Website: https://hkdsdtech.com/
Tel: +852-51679050
Email: customer@hkdsdtech.com

Stop Writing Boilerplate: Comparing AI Plugins vs. AI-Native IDEs for Java

Dragonsoft DevSecOps — Tue, 24 Mar 2026 03:11:38 +0000

[Summary]
Artificial Intelligence is reshaping the Java development workflow at an unprecedented pace. This comprehensive guide explores the core use cases of AI in Java development and provides an in-depth comparison between traditional IDE AI plugins (for IntelliJ IDEA, VS Code) and the new wave of AI-native IDEs (like Cursor, Windsurf, Kiro). Brought to you by Dragonsoft, an authorized partner of both JRebel and JetBrains, this article will help you discover the right AI coding approach for your team and learn how to eliminate Java redeploy wait times to truly maximize your developer productivity.

Java developers have always adapted to new tooling: from the first IDEs, to build automation, testing frameworks, and beyond. Now, AI is making its way into every part of the Java development workflow, and the options are multiplying fast.
The question is no longer whether to adopt AI in your Java development practice, but which approach makes the most sense for your business and how you work. This guide covers how AI writes Java code, where it adds the most value, and how today's AI-powered tools — from traditional IDE plugins to fully AI-native IDEs — compare.

Why Java Developers Should Use AI Tools

Writing an application in Java involves a lot of repetitive work. Boilerplate code, getter/setter generation, null-checks, and unit test scaffolding are all tasks most developers can complete with their eyes closed — but they still take time away from other tasks.

Direct Time to Higher-Value Work
Generative AI tools address this time sink directly. By automating the rote parts of coding, developers can redirect energy toward higher-value work: improving application architecture, solving complex bugs, and building features that matter to their business. When AI handles routine code generation, developers stay focused on more complex tasks.

Improve Java Code Quality
Beyond speed, AI tools also improve code quality. They surface potential bugs early, suggest more efficient patterns, and catch issues that might otherwise slip through code review. For Java teams working across microservices and remote development environments, that kind of assistance compounds across every sprint.

Looking for another way to improve Java code quality? Try XRebel for free.

What Is Vibe Coding?
Vibe coding is when a Java developer writes an AI prompt in plain language and the generative AI tool returns working Java code.

How Does Generative AI Write Java Code?

Generative AI tools write Java code by interpreting natural language prompts through large language models (LLMs). These models are trained on large datasets of existing code, which means they recognize common Java idioms and standard design patterns. The output reflects those learned patterns.
That said, AI-generated code is not infallible. Because the models learn from existing code samples, they can inadvertently replicate errors, outdated practices, or suboptimal patterns. Developers should always review, test and validate code developed through vibe coding.

Use Cases for AI in Java Development

AI might not be able to write an entire Java application from start to finish (yet), but it can help automate some of the more tedious processes or augment tasks that are prone to error.

Intelligent Code Completion
Intelligent code completion tools analyze the full context of your code — the class structure, method signatures, variable names, and surrounding logic—and predict what you are likely to write next. This includes completing entire statements, suggesting method implementations, and offering contextually relevant snippets from across the codebase.
The practical result: fewer keystrokes, less context-switching, and faster iteration through repetitive code patterns.

Error Detection and Bug Fixing
Traditional static code analyzers, like Klocwork, identify many common issues in their application, but AI-enhanced tools can detect more complex problems by learning from a vast dataset of coding errors and fixes. By learning from a vast dataset of coding errors and their corresponding fixes, AI-powered tools can detect more nuanced bugs such as logic errors, concurrency issues, and edge cases that traditional analyzers often miss.
Some tools go further, not only flagging issues but applying fixes automatically, reducing the time developers spend diagnosing and resolving problems.

Refactoring and Code Optimization
AI tools can analyze existing code for inefficiencies, redundancies, and violations of best practices to suggest improvements. This is particularly valuable for legacy Java codebases, where technical debt accumulates over time.
Rather than manually reviewing every method for optimization opportunities, developers can use AI to surface the highest-impact refactoring targets and apply changes with confidence.

Unit Test Generation
AI agents can suggest test cases based on historical data, coding patterns, and best practices, saving developers significant time and effort. This not only accelerates the development cycle but also allows Java developers to focus on writing feature-rich code instead of unit test generation.

Code Review
AI agents can help reduce the time required for manual reviews by detecting patterns and suggesting improvements (e.g., optimizing algorithms, refactoring redundant code, or enhancing readability).

AI-Native IDEs vs. AI Plugins for Traditional Java IDEs

This is where the Java development landscape splits into two distinct approaches for using AI tools to write Java code.

AI plugins for traditional IDEs add AI capabilities on top of an existing environment. Developers keep the IDE they know and love (e.g., IntelliJ IDEA, VS Code, or Eclipse) and install a plugin that provides features like code completion, chat-based assistance, and error detection. The IDE itself remains the primary interface; the AI is an assistant within it.
AI-native IDEs, on the other hand, are built from the ground up with AI at the center. The AI doesn’t just respond to prompts — it actively plans, writes, refines, and tests code as an autonomous collaborator. Developers describe goals in plain language, and the agent takes multi-step action to achieve them. AI-native IDEs manage context, tracks changes, and handles tasks with a level of autonomy that AI plugins for traditional IDEs like IntelliJ, Eclipse, and VS Code cannot match.

Looking to save more time within you Java IDE of choice? Try JRebel for free for 14 days.

Both approaches have their place. Plugins offer lower friction that help teams maximize their investment in an existing IDE (e.g., IntelliJ). AI-native IDEs offer greater capability for multifunctional coding assistance that’s directly integrated within development workflows.

How AI-Native IDEs Change Development Workflow

In a traditional Java development loop, a developer writes code, waits for a build, runs tests, and then fixes errors. Even when using JRebel to eliminate redeploys and XRebel to spot performance issues faster, that process still takes time. In an AI-native IDE, that entire development workflow is automated with minimal developer involvement required.

Traditional Java Development Loop

AI-Native IDE Development Loop

AI Plugins for IntelliJ IDEA, VS Code, and Eclipse

There are some key advantages in using an IDE-based AI code generator — the most important being that the Java developer doesn’t need to break their flow, leave the IDE, or rely on copious amounts of copy/paste. This can reduce the opportunity for errors while increasing productivity.

JetBrains AI in IntelliJ IDEA
IntelliJ IDEA remains the most widely used Java IDE, and JetBrains has built AI directly into the platform. JetBrains AI Assistant provides unlimited code completion, a chat interface for asking questions about the codebase, and access to multiple LLMs — including models from OpenAI, Anthropic, and Google, as well as JetBrains' own Mellum model, which is optimized specifically for code completion.
Because the AI agent is embedded natively in the IDE, it has full context awareness. Developers can ask short, direct questions rather than crafting detailed external prompts. JetBrains also offers Junie, an agentic coding assistant that can plan, write, and test code directly within IntelliJ IDEA, further bridging the gap between a plugin and an AI-native IDE.

GitHub CoPilot in Microsoft VS Code
GitHub Copilot is one of the most widely adopted AI coding assistants and integrates cleanly into VS Code via extension. It provides inline code suggestions, natural language chat, terminal assistance, and the ability to ask questions about unfamiliar codebases. Copilot is language-agnostic, which makes it a practical choice for developers working in polyglot development environments.

GitHub Copilot in VSCode offers several features, including:

Offering developers to get inline code suggestions while they’re writing and iterating code
Fixing errors in code
Resolving errors while running commands in the terminal
Asking questions to help ramp-up on a new code base
Using chat features to configure your VS Code setup

AI in Eclipse
Eclipse has integrated AI capabilities to enhance its error detection features. This feature is used by analyzing code patterns and comparing them against known bug patterns, allowing Eclipse developers more precise and actionable insights. plugins that lend AI functionality, including Tabnine, aiXcoder, and Remain Ai Chat.

Comparing AI-Native IDEs: Cursor, Windsurf, Kiro, and Antigravity

AI-native IDEs are built with AI embedded as the core component, but each functions a little bit differently. Learn more about the differentiators between Cursor, Windsurf, Google Antigravity, and AWS Kiro.

Cursor
Cursor is an AI-native IDE built on a VS Code fork; it has gained significant traction among developers who want deep AI integration. Cursor’s composer agent handles multi-step tasks autonomously: planning implementations, writing code across multiple files, running tests, and iterating based on results. Cursor also includes a specialized tab model for highly accurate autocomplete and integrates with external services like GitHub and Slack.

Windsurf
Windsurf, developed by Codeium, markets itself as the most intuitive AI coding experience. Its Cascade agent combines codebase understanding, real-time awareness of developer actions, and a broad set of tools to maintain a "flow state" during development. Windsurf also supports MCP (Model Context Protocol) for connecting external tools and services, automatic lint fixing, and drag-and-drop image input to generate UI code.

Kiro
AWS has entered into the AI-native IDE space with Kiro. It is designed to work closely with AWS services and is aimed at developers building cloud-native applications. Kiro focuses on spec-driven development — developers define requirements, and the agent generates code aligned to those specs — making it particularly well-suited for teams with highly structured development processes.

Antigravity
Antigravity is an emerging AI-native IDE focused on autonomous code generation and long-horizon task completion. It is designed for developers who want to delegate entire features or workflows to an AI agent, with the IDE handling planning, implementation, and testing as a unified process.

Final Thoughts

There are advantages to both AI plugins for IntelliJ, VS Code, and Eclipse, and AI-native IDEs like Cursor, Windsurf, Kiro, and AntiGravity — it all comes down to your organization’s Java development needs. The good news is that JRebel can eliminate Java redeploys in all popular Java IDEs.

JRebel allows you to visualize code changes instantly while maintaining application state. That means the productivity gains from AI code generation don’t stall at the redeploy step. By using JRebel, developers iterate faster, test more frequently, and stay in flow.

While AI tools reduce the time it takes to write Java code, JRebel reduces the time it takes to see the results.

Compound Your Time Savings With JRebel

AI and JRebel address different parts of the development cycle, and together, they close the loop on two of the most time-consuming parts of Java development: writing Java code and seeing it run in your business application.

Contact Dragonsoft—Authorized partner for JetBrains and JRebel—to start your free trial of JRebel today, and multiply the productivity gains of your AI-powered Java workflow!

Atlassian Data Center EOL is Coming. Here’s Why Cloud is Actually Better for Dev Teams.

Dragonsoft DevSecOps — Tue, 24 Mar 2026 02:24:00 +0000

[Abstract]
With Atlassian officially announcing that the Data Center (DC) will reach its End of Life (EOL) on March 28, 2029, the enterprise journey to the Cloud has entered its final three-year countdown. Today, the underlying maintenance costs, system upgrade downtime risks, and the lack of native AI capabilities in traditional Data Center environments are increasingly becoming bottlenecks to improving development efficiency.
In contrast, Atlassian Cloud not only offers a maintenance-free, zero-downtime infrastructure but also exclusively introduces the Rovo AI, no-code Automation, and global data insights (Atlassian Analytics). This article will break down the core experience differences between Atlassian Cloud and Data Center, revealing why top global teams like Mercedes-Benz and Air France-KLM are abandoning DC for the Cloud.
As an Atlassian Platinum Solution Partner, Dragonsoft looks forward to partnering with you to smoothly launch your Cloud transformation journey.

The Cloud Advantage: Making Work Easier

1. Collaborate seamlessly across all Atlassian apps
Easily collaborate, search, and manage work without switching between different tools.

Search across apps
Find issues, pages, and updates across Jira, Confluence, and other cloud apps from a single search bar.
Set and track team goals
Use the Goals app to track shared goals your way, connect them to outcomes, and communicate progress.
Instant view of work
The Home and Teams apps in cloud give every user a personalized view of recent work, project teams, and priorities.

2. Simplify admin and security with builtin maintenance
Offload the burden of manual server maintenance to Atlassian Cloud, allowing your IT team to say goodbye to manual upgrades and focus on high-value strategic work while ensuring enterprise-grade data protection.

Stay current with automatic upgrades
Cloud updates automatically, so your team always has the latest features, with no downtime.
Protect your data with Atlassian Guard
Atlassian Guard protects your data with built-in security and threat detection across all cloud products.
Manage all your users from a single dashboard
Set up and adjust users across all your Atlassian apps from one simple dashboard, with Atlassian Administration.

3. Make informed decisions with connected data
Get instant insights with Atlassian analytics, instead of putting together reports from separate tools.

See your team’s data in one place
Easily access data from Atlassian Data Lake and external sources to get a unified view of your work.
Get insights with natural language
Generate SQL queries using natural language to quickly get the insights you need.
Share and collaborate with analytics
Comment, tag teammates, and share dashboards that combine cross-product data.

4. Automate tasks across your apps without code
Set up automations across products with just a description of what you want to automate.

Automate tasks without code
Create automations with natural language prompts in Rovo Studio with no coding skills needed.
Set up automations across products
Set up automations across cloud apps to keep teams up to speed, no matter where they’re working.
Automate with third party apps
Automate third party tools like Slack or Google Workspace with Atlassian’s cloud products.

Features Only-in-Cloud: Unlock AI and New Experiences

1. Simplify your data landscape
Make sense of your scattered app data on a single platform with Atlassian robust data layer.

Find related work and information
Find work, goals, and decisions across all cloud apps with Teamwork Graph-powered search.
Bring all your tools together
Bring together data from Atlassian’s and 100+ connected apps.
Get accurate answers with AI
Rovo, Atlassian’s AI tool, uses Teamwork Graph to create answers based on how your teams work.

2. Add AI to the team
Use Rovo, Atlassian’s AI teammate, to search and chat, instead of looking through individual apps to find answers.

Find what you need with Rovo Search
Use Rovo Search to find the info you need across Atlassian and 50+ tools.
Use Rovo Chat for incontext answers
With Rovo Chat, ask a question or upload a file and receive contextaware answers.
Automate routine tasks with Rovo Agents
Build your own Rovo Agent to triage issues or summarize content, no coding required.

3. Try innovative cloud-first apps and collections
Add new functionality to your Atlassian toolchain continuously without relying on 3rd party apps.

Reduce unnecessary meetings
Use Loom to record and share video updates so you can replace meetings.
Plan and launch new products easily
Capture and prioritize ideas, organize user feedback, and plan roadmaps.
Create and track your team’s strategy
Use the Strategy Collection to align goals, plans, and progress in one hub.

“Atlassian cloud is the solution for high performance, high security, high avaiability, and the newest features.”
MEHMET SARI
Modern Collaboration Platform Team, Mercededs-Benz

“Other teams often come to us, asking to upgrade to Atlassian cloud because they see how it
could improve their life and deliver a better experience. Now, we have more non-developer users on Atlassian than developers.”
NATHAN WATTIMENA
Scrum Master - RTE Next Generation Data Center
Air France-KLM

Ascend to new heights with Atlassian Cloud

Data Center end of life will take place on March 28, 2029. Data Center subscriptions and any associated Marketplace apps will expire on this date. To make this transition as smooth as possible, Atlassian is winding down support in phases across the next 3 years, giving you plenty of time to plan your next steps. It’s time to ascend to new heights with Atlassian Cloud. Your success is our mission

Reduce risk and accelerate migration timelines
Ensure compliance, performance, and scalability
Customize your cloud environment for enterprise needs

Ready to accelerate your journey? Let's map out your Cloud transformation together. Contact Atlassian Platinum Solution Partner Dragonsoft today to get started!

Unlock the True Speed of AI Development: SonarQube 2026.1 LTA is Here

Dragonsoft DevSecOps — Fri, 27 Feb 2026 07:44:42 +0000

Reach the true speed of AI development

The 2026.1 LTA release represents a fundamental shift in how teams develop software. As AI-generated code becomes a standard and AI-native IDEs and agents become a common component of the development lifecycle, the “vibe, then verify” philosophy has moved from a recommendation to a necessity.

The 2026.1 LTA release unifies the analysis of human-written, AI-generated, and 3rd party code into a single, high-performance verification layer that integrates deeply into the modern developer workflow.

With enhanced security for the software supply chain, expanded compliance coverage for safe and secure systems, and groundbreaking analysis speed for popular languages like Python, Java and JavaScript/TypeScript, SonarQube Server 2026.1 LTA is designed to confidently drive engineering velocity without compromising code health.

Ready for the AI and agentic SDLC

AI and agentic coding assistants are powerful tools, but their output requires a verification layer to prevent the introduction of new code quality or code security risks.

AI-native IDE integrations: SonarQube now works with Claude Code, Cursor, Windsurf, and Gemini solve the verification bottleneck by bringing deep code intelligence directly into the modern developer workflow.
SonarQube MCP Server: AI agents can now query your SonarQube Server instance for code quality and security insights to ensure AI-generated code is production-ready.
AI CodeFix (BYO LLM): Leverage the power of your own Azure OpenAI service to generate AI CodeFix suggestions. This “Bring Your Own Model” approach ensures your source code stays within your private, secure environment, meeting the strictest data privacy and intellectual property requirements.
AI CodeFix in the IDE: Move from identification to remediation instantly. AI-generated fix suggestions are now available directly in VS Code and IntelliJ, allowing software developers to resolve code quality and security issues with a single click within their primary workspace.

Enhanced code security

Modern security requires moving beyond simple pattern matching. SonarQube provides deep, context-aware analysis that identifies how data flows through an application, uncovering security vulnerabilities that other tools miss.

Advanced supply chain protection
The software supply chain is the new frontier for cyberattacks. SonarQube now offers a comprehensive suite of static code analysis tools to secure every dependency.

Advanced Security (SCA and SBOM): Proactively secure your software supply chain with Software Composition Analysis (SCA) and SBOM vulnerability detection. This is now available across a comprehensive range of languages, including Java, Python, C#, C, C++, JavaScript, TypeScript, Go, Rust, Ruby, and PHP.
Malicious package detection: (New for the 2026.1 release, available in Advanced Security) Protect your organization from sophisticated supply chain attacks. The SCA capability in SonarQube Advanced Security now raises blocker-level alerts when it detects malicious upstream open-source packages from the OSSF dataset, preventing secret exfiltration and data breaches before they can impact your environment.
GA of SCA for C/C++: (Now generally available for the 2026.1 release, available in Advanced Security) SonarQube have extended our deep SCA capabilities to C and C++ projects, allowing teams working in performance-critical environments to manage their dependency risks with the same rigor as other modern stacks. Developers using Conan and vcpkg now receive automated dependency risk feedback.
SCA in the IDE: (available in Advanced Security) Eliminate context switching by bringing dependency risk visibility directly to the developer. Vulnerability and license information for open-source packages is now visible within Visual Studio, IntelliJ, and VS Code, enabling remediation as code is written.
SBOM Import (beta): (available in Advanced Security) CycloneDX and SPDX SBOMs to report vulnerabilities for arbitrary apps, containers, and C/C++. This gives you universal dependency coverage, gaining visibility into vulnerabilities within containers and third-party components that were previously opaque.

Deep application security (SAST)

Refreshed advanced SAST: (New for the 2026.1 release, available in Advanced Security) Our advanced SAST has been optimized for the libraries developers use most. Java and C# have been refreshed for the top 1,000 public libraries for each language. Python has been refreshed for the top 100 libraries. This ensures that security findings are highly relevant and accurate, based on the actual libraries used by most teams.
Expanded language security: SonarQube have introduced full SAST with taint analysis for Go and Kotlin, plus taint analysis for VB.NET, and we’ve added SAST for Swift and Dart to help mobile developers identify complex data flow vulnerabilities.
Pipeline and infrastructure security: Fortify your CI/CD pipeline environments with new analysis for GitHub Actions and Bash/Shell scripts. SonarQube identify misconfigurations, unsafe file permissions, and insecure commands in .sh files to ensure your delivery pipelines are as secure as your application code.
Secrets detection: With over 450 distinct secret patterns and support for 60+ cloud applications, SonarQube provide best-in-class protection against credential leaks. This coverage now extends to YAML, JSON, and CLI files.

Reliable code quality and maintainability

Swipe away issues that cause code bugs and technical debt and boost developer productivity. The 2026.1 release includes smarter engines that understand the intent and structure of your code.

Language-specific optimizations

Python: Maximize performance and readability by finding issues for coroutines, comprehensions, and AWS Lambda function optimization. Parallelized analysis now delivers a massive analysis speed boost for Python automated reviews, cutting feedback loops significantly.
Java: Our advanced Dataflow Bug Detection (DBD) engine now handles complex bugs, such as null-dereferences and division-by-zero, across multiple function calls. SonarQube finds issues that help developers avoid common pitfalls and performance bottlenecks in the Spring framework.
JS/TS: A next-generation taint analysis engine improves both accuracy and speed. Developers will benefit from finding issues for Angular and accessibility (a11y) standards, alongside up to a 40% speed improvement for large project analysis.
Kotlin: We’ve added support for the new Kotlin 2.0 language and the K2 compiler including a 50% boost in Kotlin analysis speeds.
Faster remediation: (New for the 2026.1 release) SonarQube have released over 80 new QuickFixes for core JavaScript and TypeScript rules in SonarQube for IDE to help you resolve issues with a single click. These automated suggestions allow you to remediate issues in real time, as you code using SonarQube IDE.

Expanded standards compliance

For organizations in highly regulated industries, manual compliance checks are a bottleneck. SonarQube automates the collection of evidence and provides reports for global safety and security standards.

Safety-critical systems

MISRA C++:2023: SonarQube includes complete coverage for safety-critical systems. SonarQube now enforce all 179 guidelines for the C++17 standard, providing the necessary guardrails for automotive, aerospace, and medical device software.
MISRA in the IDE: Shift compliance left by displaying MISRA detection results directly in VS Code, Visual Studio, and IntelliJ/CLion. This allows developers working on mission-critical code to catch violations in real-time, long before the code is committed.
Modern security benchmarks
OWASP MASVS: (New for the 2026.1 release, Enterprise/Data Center only) Specifically designed for mobile developers, this feature assesses compliance against the OWASP Mobile Application Security Verification Standard, ensuring mobile apps are resilient against modern threats.
OWASP Top 10 for LLM: (New for the 2026.1 release, Enterprise/Data Center only) Secure your AI-powered applications with dedicated reporting on the 10 most critical vulnerabilities specific to coding Large Language Models, including prompt injection and insecure output handling.
Updated industry standards: Stay compliant with reports for the CWE Top 25 2024, OWASP Mobile Top 10, and the STIG V6R3 standard.
WCAG: Transform accessibility (a11y) compliance from a late stage developer bottleneck into early stage detection with SonarQube’s coverage of the WCAG 2.1 AA and 2.2 AA standards. Organizations can proactively manage legal risk and build inclusive products from the start.

Broader language coverage

SonarQube continues to expand its language reach, ensuring that as your tech stack evolves, your code intelligence platform evolves with it.

Rust support: SonarQube have introduced full analysis for Rust, including native integration with the Clippy linter. This allows teams to leverage Rust’s memory safety benefits while maintaining SonarQube’s rigorous quality standards.
Latest language versions: Coverage is expanded to Java 22/23/24, Dart 3.8, Swift 5.9–6.2, and Python 3.14.
Full support for C#14 and .NET 10: (New for the 2026.1 release) Safely adopt the latest language advancements like the field keyword and null-conditional assignments with confidence. Additionally, we’ve updated over 300 rules to eliminate false positives, so you get improved actionable code intelligence without the noise of incorrect alerts.
AI/ML and data science assurance: Ensure the reliability of your data pipelines with support for PySpark and PyTorch. Data scientists can now analyze their code directly within Jupyter Notebooks in PyCharm.
Apex and Ruby on Rails: Enhanced coverage for enterprise-scale Salesforce (Apex) and web applications (Ruby on Rails) ensures broad coverage across the organization.
More new languages: Over the last year, SonarQube added support for YAML, Bash/Shell, JSON, and Github Actions, to help you get the best out of your code throughout the CI/CD pipeline.

Deeper DevOps integrations

Platform engineering teams need tools that go where developer teams work. Eliminate the copy-paste actions or custom scripting to push updates to common workplace tools.

JFrog: (New for the 2026.1 release, Enterprise/Data Center only) Streamline your software package audit trail by automatically pushing SonarQube quality and security evidence directly to JFrog. This creates a single source of truth for software attestation, eliminating disruptions in the delivery pipeline as a result of auditing.
Jira: Transition from code review to task management by pushing SonarQube issues directly into Jira as tickets.
Slack: Stay aligned with real-time quality gate status notifications delivered directly to your team’s Slack channels.
Optimized platform operations
Enterprise infrastructure: Operate SonarQube Server in IPv6-only environments to support massive-scale container and serverless workloads.
In-app product news: Keep your teams informed with product news and update alerts displayed directly within the SonarQube Server interface, ensuring everyone is up to speed with the latest changes.
Seamless updates (Sandbox): Many of you asked for it and now it’s here. Perform version updates of the SonarQube Server without the fear of your quality gates changing status. New issues found on existing code during an update due to rule changes are automatically “sandboxed,” preventing them from immediately impacting your quality gates during the update.
NEW UPDATE STEP: Configuring the sandbox is a vital new step during the update to the new 2026.1 LTA. If you want to use the sandbox feature, an administrator must enable the sandbox before performing the first post-update analysis. This will ensure your quality gates aren’t impacted from the rule changes during the update.

Are you wondering, “what is an LTA?”

Consider migrating to SonarQube Cloud. With the same enterprise-grade capabilities as Server, now is a better time than ever to make the switch. Migrating once means never having to perform another manual version update again, ensuring your team has immediate access to our latest innovations. Contact sales to discuss migrating now.

Are you still using an older version of SonarQube Server?

If you’re on a version older than 2025.1, update to SonarQube Server 2025.1 LTA before updating to the latest 2026.1 LTA. Check out our helpful LTA Update Hub to plan a smooth and successful update.

To learn more about SonarQube or to apply for a free trial, please contact Dragonsoft, an authorized SonarQube partner.
Phone: +852-51679050; +852-62678513
Email: customer@hkdsdtech.com

Empowering High Velocity Service Management Across Your Organization

Dragonsoft DevSecOps — Sat, 14 Feb 2026 06:31:11 +0000

Watch the video to learn:

Challenges in ITSM and Modern Incident Management:
Understand how legacy models hinder business with siloed tools and slow processes, and why aiming for "zero incidents" is a misconception.
The Unified Solution for High-Velocity Service Management:
Discover how to unite Dev, IT Ops, and Business teams on a single, AI-powered platform—Atlassian Jira Service Management—for efficient collaboration.
DragonSoft's Localized Services for Success:
Learn how DragonSoft's expert consulting, implementation, and training services ensure the successful adoption and optimization of Jira Service Management, strengthening your IT operations with tailored support.

👉For more information, please contact Dragonsoft at +852-5167-9050 or email us at customer@hkdsdtech.com

Webinar Recap | Unlock Smarter Service Management with AI: Jira Service Management + Rovo Practical Guide

Dragonsoft DevSecOps — Sat, 14 Feb 2026 06:29:11 +0000

Previously, the webinar “Unlock Smarter Service Management with AI: Jira Service Management + Rovo Practical Guide”, hosted by Atlassian Platinum Partner Dragonsoft, was successfully concluded.

During the session, Zhang Xiaole, Senior Technical Consultant at Dragonsoft, delivered a keynote presentation covering the following topics:

Common challenges in service management
Core capabilities of Jira Service Management (JSM)
Practical applications of AI in service support
Atlassian Cloud data security strategies
New employee onboarding demo: showcasing how JSM + AI can significantly improve service efficiency

🔎Watch now and discover how Atlassian AI can double the efficiency of your IT service desk!
👉Read the full recap here: https://hkdsdtech.com/resources/review-of-the-jsm-ai-practical-guide-seminar