DEV Community: Amita Sharma

# AI Is Quietly Replacing One of the Most Expensive Parts of Software Engineering

Amita Sharma — Mon, 10 Aug 2026 10:44:09 +0000

For the last two decades, software testing has followed a predictable cycle: write test cases, automate them, maintain them, fix them after UI changes, investigate flaky failures, and repeat.

The industry called this progress.

AI is exposing how expensive that process actually was.

The biggest shift in software engineering right now isn't that AI can write code. It's that AI can increasingly understand applications, generate tests, adapt to interface changes, and recover from failures without constant human intervention.

This is not another automation upgrade.

It is a fundamental change in the economics of quality assurance.

The Hidden Cost of Test Automation

Most engineering leaders assume automation becomes cheaper over time. In reality, automation often becomes a maintenance problem.

Multiple industry studies estimate that 30–50% of all test automation effort is spent maintaining existing scripts rather than creating new test coverage. In mature engineering organizations, automation engineers frequently spend more time updating selectors, repairing broken workflows, and rerunning failed pipelines than testing new product functionality.

That means a significant portion of QA investment is directed toward preserving automation, not improving quality.

The Flaky Test Tax

One of the least discussed costs in software engineering is flaky automation.

Large engineering organizations have consistently reported that 30–40% of automated test failures are caused by flaky tests rather than actual product defects. These failures trigger unnecessary investigations, pipeline reruns, delayed releases, and lost engineering time.

Imagine a SaaS company running 50 CI pipelines per day with 500 automated tests. With a 35% flaky failure rate and just 10 minutes spent investigating each false failure, the organization loses nearly 29 engineering hours every day. Over a year, that exceeds 7,000 engineering hours spent diagnosing problems that never existed.

Why AI Is Different

Traditional automation relies on explicit instructions. A test script must know exactly which selector to click, which field to fill, and which element to verify.

AI-based testing systems operate differently. They understand intent and context.

Instead of relying solely on brittle selectors, modern AI systems can reason across visual layout, text labels, accessibility attributes, historical behavior, and application structure. When an interface changes, they can often infer what the user intended rather than simply failing.

This is the difference between script execution and application understanding.

The Rise of Autonomous QA

The industry is already moving beyond basic self-healing automation.

AI-driven testing systems can increasingly generate test cases from product behavior, create edge-case scenarios automatically, recover from UI changes, execute tests across environments, diagnose failures, and even regenerate broken workflows.

The workflow is shifting from:

Write → Maintain → Rewrite

to:

Describe → Execute → Adapt

That is a fundamentally different operating model.

AI Adoption Is Accelerating

Software engineering has become one of the fastest AI-adopting industries in history.

Recent developer surveys show that well over half of professional developers now use AI tools in their daily workflow, and AI-assisted development has become mainstream across coding, debugging, documentation, and code review.

Testing is following the same trajectory.

Analysts increasingly project strong double-digit growth across AI-driven testing and quality engineering markets through the remainder of the decade, driven by cloud software, continuous delivery, and the rising cost of manual QA.

The question is no longer whether AI will participate in testing.

The question is how much of the testing lifecycle will remain human-operated.

The Economics of AI-Driven Testing

Consider a team with 15 engineers, 3 QA engineers, weekly production releases, and 1,000 automated tests.

If maintenance consumes 40% of QA capacity, and AI reduces that maintenance burden by 35%, the team recovers hundreds of engineering hours every year. Those hours can be redirected toward faster regression cycles, broader test coverage, quicker releases, and new feature validation instead of repairing old automation.

The return on investment is not simply fewer hours.

It is greater engineering throughput.

Why SaaS Teams Should Pay Attention

Startups often postpone automation because writing and maintaining tests feels expensive.

Enterprises struggle because maintaining thousands of tests becomes expensive.

AI changes both constraints simultaneously.

Smaller teams gain broader coverage without building massive automation suites. Larger teams reduce maintenance overhead without rebuilding their entire testing infrastructure from scratch.

This is why the conversation is shifting from automation frameworks to AI testing agents.

The Next Competitive Advantage

The software industry has already watched AI transform code generation, customer support, design, analytics, and documentation.

Quality assurance is now entering the same phase.

The companies that ship the fastest over the next five years will not necessarily be the ones with the largest QA teams.

They will be the ones whose testing systems can understand products, adapt to change, and improve continuously.

The future of QA is not more scripts.

It is less maintenance, more intelligence, and software that can increasingly test software on its own.

That shift has already begun.

The only real question is how long teams can afford to keep maintaining automation that AI is learning to replace.

Stop Wasting Hours on Testing | Get 3 Months of Automation Free (July 23 to August 22)

Amita Sharma — Thu, 23 Jul 2026 04:10:45 +0000

Stop Wasting Hours on Testing | Get 3 Months of Automation Free (July 23 to August 22)

Testing should not slow your team down. But for most teams, it still does.

Across the industry, QA teams spend a significant amount of time on repetitive testing, maintaining scripts, and fixing broken automation. Studies suggest that up to 40% of testing effort goes into maintenance instead of actual validation. That means slower releases, delayed feedback, and higher chances of bugs reaching production.

Now imagine a workflow where testing is fast, simple, and does not require constant effort.

What is QAlity?

QAlity is a no code test automation platform built for fast moving teams. It allows you to record test steps, execute them seamlessly, and analyze results without writing complex scripts.

Instead of spending hours building and maintaining automation frameworks, teams can focus on running tests and improving product quality. Everything is designed to reduce effort and increase speed.

Teams adopting modern automation approaches have reported up to 60% faster testing cycles and significantly improved release confidence.

Why This Offer Exists

One of the biggest reasons teams avoid automation is complexity.

Traditional tools require setup, coding knowledge, and ongoing maintenance. This creates a barrier, especially for teams that want quick results.

We want you to experience a simpler approach.

That is why we are offering full access so you can try QAlity in your real workflows and see the difference without any commitment.

The Offer

From July 23 to August 22, you can unlock:

3 Months of full test automation access completely free

This offer can only be availed within this time window. Once activated, you will get full access for the next 3 months.

No payment required. No hidden conditions. Just activate and start using.

What You Can Do

With this access, you can:

Automate test cases without writing code
Execute tests faster across workflows
Reduce repetitive manual testing
Improve team productivity
Catch bugs earlier and release with confidence

Teams that shift to smarter automation do not just save time. They build faster, release more frequently, and reduce production issues.

How to Get Started

Getting started is simple:

Register on QAlity
Log in to your account
Go to the Notifications section
Verify your email
Click on Activate Offer

Once activated, your access starts instantly.

Important Dates

Start Date: July 23
End Date: August 22

Make sure you activate your offer within this period to enjoy the full 3 months of access.

Try It Without Risk

Most teams delay automation because it feels like a big change.

This is your opportunity to try it without any risk.

No cost. No commitment. Just real results.

AI Products vs. AI Features: The Next Big Tech Battle

Amita Sharma — Wed, 22 Jul 2026 12:43:19 +0000

It feels like AI is competing against itself but in two very different forms.

On one side are standalone AI products like ChatGPT, Claude, Gemini, and Perplexity. On the other are AI-powered features quietly making the software we already use smarter.

The question isn’t whether AI is the future. It’s whether the future belongs to AI as a product or AI as a feature.

This debate was recently highlighted by popular tech creator MKBHD (Marques Brownlee), who compared AI’s evolution to the rise of social media Stories. Snapchat pioneered Stories as its core product, but the feature eventually spread across Instagram, Facebook, WhatsApp, LinkedIn, and countless other platforms. While Snapchat introduced the idea, billions of users ultimately experienced Stories through products they were already using.

AI appears to be following a remarkably similar path.

When ChatGPT launched in late 2022, it became one of the fastest-growing consumer applications in history. It proved that people were willing to use AI as a destination — for writing, coding, research, brainstorming, and problem-solving. Since then, products like Claude, Gemini, Perplexity, Mid journey, and others have shown that AI-native applications can attract millions of users on their own.

At the same time, some of the world’s largest technology companies have taken a different approach.

Rather than building separate AI applications, they’re embedding AI into products people already rely on every day.

Microsoft integrated Copilot across Microsoft 365, Windows, GitHub, and Dynamics. Google brought Gemini into Workspace, Android, Search, and Chrome. Adobe added Firefly to Creative Cloud. Salesforce introduced Einstein AI across its CRM platform. Notion, Canva, Atlassian, Slack, Zoom, and many SaaS companies have done the same.

Perhaps the most interesting example is Apple.

When Apple entered the AI race, it didn’t launch a ChatGPT competitor. Instead, it introduced Apple Intelligence a collection of AI capabilities deeply integrated into iPhone, iPad, and Mac. Apple made AI part of the operating system rather than a separate destination.

That decision says a lot.

If one of the world’s most valuable technology companies believes AI creates the most value inside existing products, it reinforces a growing industry trend: users don’t necessarily want another AI application — they want the software they already use to become more intelligent.

This approach also lowers the barrier for software companies.

Training foundation models requires enormous investments in infrastructure, GPUs, research teams, and ongoing optimization. For most businesses, building their own large language model simply isn’t realistic.

Instead, they’re leveraging models from providers like OpenAI, Anthropic, and Google through APIs while focusing on what actually differentiates their products: solving customer problems, streamlining workflows, and delivering better user experiences.

It’s similar to how cloud computing evolved.

Few companies build their own cloud infrastructure today. They build products on top of cloud platforms because infrastructure is no longer the competitive advantage.

AI models are rapidly becoming that new infrastructure.

The value increasingly comes from how intelligently AI is integrated into products not just from the model powering it.

Does that mean standalone AI products will become less important?
Not at all.

Products like ChatGPT, Claude, Gemini, and Perplexity continue to push the boundaries of reasoning, coding, creativity, research, and AI agents. They introduce capabilities that often make their way into productivity software, developer tools, and enterprise applications months later.

In many ways, AI products drive innovation, while AI features drive adoption.

Rather than replacing one another, the two approaches are creating a cycle where each makes the other stronger.

What Does This Mean for Software Testing?

The same shift is happening in software testing.

For years, automation transformed QA by reducing repetitive manual work. AI is now taking that evolution a step further — not just automating testing, but making it smarter.

Some companies are building AI-native testing platforms where AI sits at the center of the entire testing lifecycle. Others are embedding AI capabilities into established testing tools, helping teams improve their existing workflows without replacing them.

Today, AI is being used to support testing through capabilities such as:

AI-generated test cases
Codeless automation
Dynamic test selection and prioritization
Self-healing automation
AI-powered visual testing
Failure analysis and root cause detection
Intelligent test maintenance
The industry reflects both approaches.

Platforms like Mabl position themselves as AI-native testing solutions, while companies such as SmartBear, QAlity and Applitools have integrated AI into mature testing ecosystems. Both strategies address different customer needs, and both are contributing to the evolution of modern software quality.

Ultimately, the question isn’t whether AI products or AI features will win.

The future of AI isn’t about choosing one over the other.

Standalone AI products will continue expanding what’s possible. AI-powered features will make those breakthroughs accessible inside the applications people already use every day.

The real winners won’t be the companies building the biggest models or adding the most AI features.

They’ll be the ones that solve meaningful problems and create better experiences for their users — whether AI is the product itself or simply the feature that makes the product indispensable.

[Boost]

Amita Sharma — Tue, 23 Jun 2026 07:41:45 +0000

Chitrajan Dhiman for QAlity

Jun 23

Best Selenium Alternatives in 2026: Top Testing Tools to Replace Selenium

#ai #automation #selenium #qa

4 min read

Selenium vs No-Code Test Automation: What We Learned Building QA Workflows in 2026

Amita Sharma — Wed, 10 Jun 2026 09:29:40 +0000

For more than a decade, Selenium has been the default choice for browser automation. It powers millions of test cases across startups, enterprises, and everything in between.

But in 2026, the conversation has changed.

The question is no longer whether Selenium works—it does. The real question is whether engineering teams can continue to afford the maintenance overhead that comes with traditional test automation.

After working with teams building and maintaining QA workflows at scale, we've learned that the biggest challenge isn't writing automated tests. It's keeping them alive.

Let's explore how Selenium compares to modern no-code test automation and what we've learned along the way.

The Promise of Selenium

Selenium became popular because it gave teams complete control.

With Selenium, you can:

Automate any browser interaction
Integrate with CI/CD pipelines
Build custom frameworks
Support multiple programming languages
Execute tests across different browsers

For engineering-heavy teams, Selenium remains incredibly powerful.

A typical Selenium test might look like this:

driver.findElement(By.id("email")).sendKeys("test@example.com");
driver.findElement(By.id("password")).sendKeys("password");
driver.findElement(By.id("login")).click();

Simple.

Until the application changes.

The Hidden Cost Nobody Talks About

Most teams don't struggle with creating their first automated test suite.

They struggle six months later.

As products evolve, UI elements change frequently:

Button IDs get updated
CSS selectors change
Layouts are redesigned
New components replace old ones Suddenly dozens of tests start failing.

Not because the product is broken.

Because the automation is.

Many QA teams spend more time fixing tests than finding bugs.

This creates a dangerous cycle:

Tests fail frequently
Teams lose trust in automation
Failed tests are ignored
Real defects slip into production

At that point, automation becomes a liability instead of an asset.

The Rise of No-Code Test Automation

Modern no-code platforms approach testing differently.

Instead of writing scripts manually, users can:

Record user actions
Generate test steps automatically
Reuse workflows visually
Execute tests in the cloud
Maintain tests with AI-assisted healing

The goal isn't to replace engineers.

The goal is to remove repetitive maintenance work.

Rather than writing code for every action, teams focus on validating business workflows.

For example:

Traditional approach

Create framework
Write locators
Handle waits
Manage browser setup
Debug failures

No-code approach

Record workflow
Define assertions
Run tests
Review results

The difference becomes significant as test suites grow.

Where Selenium Still Wins

Let's be honest.

Selenium isn't going anywhere.

There are situations where Selenium remains the better choice:

Highly Customized Frameworks

Large enterprises often need complex integrations with internal systems.

Selenium provides maximum flexibility.

Engineering-Driven Teams

If your QA team consists primarily of automation engineers, code-based testing may already fit your workflow.

Advanced Technical Scenarios

Complex browser manipulation, custom drivers, and deep framework integrations are often easier with Selenium.

For these use cases, no-code solutions may feel limiting.

Where No-Code Wins

This is where we've seen the biggest shift.

Faster Test Creation

A regression scenario that takes hours to automate in Selenium can often be recorded in minutes.

Lower Maintenance

Modern platforms can automatically recover from minor UI changes.

When a button moves or attributes change, tests don't immediately fail.

Wider Team Participation

Product managers, manual testers, and business analysts can contribute to automation.

Testing becomes a team effort instead of a specialized skill.

Faster Scaling

Organizations can increase automation coverage without hiring large automation engineering teams.

The Real Enemy: Flaky Tests

Every QA team has experienced this.

A test passes locally.

Fails in CI.

Passes again the next day.

Nobody knows why.

These are flaky tests.

They're one of the biggest reasons automation projects lose credibility.

Common causes include:

Timing issues
Dynamic elements
Network delays
Environment inconsistencies

Traditional Selenium frameworks often require significant engineering effort to reduce flakiness.

Modern platforms increasingly use intelligent waiting, self-healing locators, and cloud-based execution environments to address these issues automatically.

What We Learned Building QA Workflows

The biggest lesson wasn't technical.

It was organizational.

The most successful teams don't optimize for automation code.

They optimize for confidence.

The goal of QA automation isn't to have thousands of test scripts.

The goal is to release software faster with fewer defects.

If maintaining automation consumes more effort than validating the product, something is wrong.

Whether you choose Selenium or a no-code platform, ask yourself:

How much time do we spend maintaining tests?
How many people can contribute to automation?
How quickly can we create new test coverage?
How much do we trust our test results?

The answers matter more than the tool itself.

[Boost]

Amita Sharma — Wed, 03 Jun 2026 07:54:07 +0000

Are You Still Writing Selenium Scripts for Automation? - DEV Community

For more than a decade, Selenium has been the default answer to test automation. Need to automate a...

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