Why Manual Regression Testing Still Exists (And How AI Actually Helps Without Replacing You)
Look, I’ve been doing test automation for over 25 years. I’ve heard the predictions.
“Manual testing is dead.” “AI will replace testers.” “Everything will be automated.”
And yet, here we are in 2026, and guess what? Manual regression testing is still very much a thing.
When I asked Daniel and Wilhelm on my automation testing podcast about this on the show, the response was immediate and unanimous: manual regression hasn’t gone away – it’s just under more pressure than ever before.
FYI this post is based on my conversation with Daniel Garay (Director of QA at Parasoft) and Wilhelm Haaker (Director of Solution Engineering at Parasoft) – listen to the full episode [here]
The Real Problem Isn’t That Manual Testing Exists
The problem is that testers are making high-stakes decisions with almost zero visibility into what actually changed.
Daniel put it perfectly: “We don’t see the code. We don’t know what changes under the hood. So we have to define the scope of testing with our experience, our knowledge of the product.”
Think about that for a second. You’re responsible for quality, but you’re working in a black box.
You don’t know which components were touched. You don’t know which code paths are at risk. All you have is:
- Your experience
- Product knowledge
- A shrinking window of time
- And a manager who’s definitely NOT saying “Take all the time you need – quality comes first”
Daniel laughed when I brought this up because he’s never heard those words either. None of us have.
So what happens? Teams end up in one of two bad situations:
- Over-test – Run massive regression suites “just in case,” which burns time and slows releases
- Under-test – Cut scope aggressively to hit deadlines, then lose sleep wondering what slipped through
Neither option feels good. And honestly? The stress doesn’t even end when the release goes out. You’re still thinking: Did something fall through the cracks?
Why Manual Regression Won’t (And Shouldn’t) Disappear
Here’s the thing: even teams with mature test automation still rely on manual testing for:
- Complex user workflows
- Exploratory testing around changes
- Areas where automation coverage is weak or brittle
And get this, even automation engineers do manual testing first to figure out what to automate.
It’s not a fallback. It’s foundational.
Wilhelm made a great point about risk tolerance.
If you’re vibe coding a side project like me? Sure, test in production. Who cares?
But if you’re working in healthcare, finance, or any system where bugs can seriously hurt people? You can’t just YOLO it.
Manual regression becomes your last line of defense. The higher the risk, the more critical thoughtful regression testing becomes.
I used to work in healthcare. Trust me, I wasn’t vibe coding anything into production back then.
Hopefully I’m not doing it now either… but for my personal projects?
Different story.
Try Parasoft’s test impact analysis (TIA)
What “AI-Driven” Actually Means (Hint: Not What You Think)
When people hear “AI-driven testing,” they picture robots writing tests and making release decisions without humans.
That’s not what we’re talking about here.
What Daniel and Wilhelm showed me is that AI-driven manual regression is really about decision intelligence , using data to inform your choices, not replace your judgment.
The challenge isn’t execution. Testers know how to run tests.
The challenge is knowing which tests actually matter after a change.
Here’s where it gets interesting: Test Impact Analysis uses code coverage data to connect code changes with the tests that previously exercised that code.
Wilhelm explained it like this: “When code is changing from one time you ran the tests to now the test environment has a new build, you can look at data to say, based on what code changed, what subset of tests are impacted.”
This used to be one of my biggest pet peeves as an automation engineer.
Someone would check in code, and we’d run the entire suite. Tests would fail that had NOTHING to do with the change, creating noise while you’re trying to debug what actually matters.
Imagine instead: you check in code, and the system tells you “Test A, B, and Z are impacted. Everything else? Skip it.”
How Test Impact Analysis Actually Works
Okay, so here’s the technical piece (I’ll keep it practical):
Code coverage normally gets discussed as a developer metric – you capture it during unit testing. But you can capture code coverage from ANY type of testing, even browser-level system testing.
When testers execute manual tests, code coverage data gets collected in the background. That establishes a relationship between each test and the parts of the application it touches.
Then when code changes, the system can identify:
- Which tests are impacted by the change
- Which tests provide unique value vs. overlapping coverage
- Which tests are completely unaffected
The result? You’re making regression decisions based on evidence, not vibes.
As Wilhelm said: “It’s not vibes, it’s not feelings, it’s not committee.
You’re looking at code coverage data and code changes, and the system tells you these are the tests that have been impacted.”
This Works for Automated Tests Too
Although we’ve been focusing on manual regression, the same approach works for automation.
Large test suites have their own problems, long execution times, flaky tests, high maintenance costs.
Running every automated test on every build creates more noise than insight.
Test Impact Analysis lets you apply the same change-based logic.
Only run the automated tests affected by recent code changes for a given build. This shortens pipelines, reduces maintenance pressure, and delivers faster feedback to developers.
Instead of manual and automated testing competing for resources, they reinforce each other, both guided by the same data.
Real Benefits Teams Are Seeing
When teams move away from guesswork-driven regression, they typically see:
Faster releases without sacrificing confidence – You’re not cutting corners, you’re cutting redundancy
More focused effort aligned to actual risk – Time spent where it matters, not where it doesn’t
Better QA-dev collaboration – Shared understanding of what changed and what’s affected
Less stress – Daniel emphasized this: “If you have data-driven information that tells you this is what needs to be validated, it reduces stress. You get that peace of mind that you’re good with what you’re covering.”
That peace of mind piece is huge. The anxiety testers carry around release time isn’t just about workload – it’s about uncertainty. Data removes that uncertainty.
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What Tech Does This Support?
Wilhelm covered the technical details: the solution supports Java and C# (think Spring Boot applications, .NET world). It’s test framework agnostic, so you can plug it in with whatever framework you’re using in CI/CD.
From a deployment perspective, it’s a web server that supports Kubernetes if you want to containerize it. You can import tests from tools like Jira X-ray or Azure DevOps test plans.
There’s some DevOps work involved – the coverage agent needs to be deployed with your application in test environments. But according to Wilhelm, it’s pretty reasonable to set up.
My Take
Look, I’m not saying AI is going to solve all your testing problems. And i know there is a lot of hype around AI test automation tools.
But here’s what I am saying:
Manual regression testing exists because software complexity, risk, and human judgment still exist. That’s not changing.
What IS changing is how teams decide where to spend manual effort. And moving from guesswork to data-backed decisions? That’s not hype. That’s just smart.
The future of manual regression isn’t about doing more work faster. It’s about doing the right work, for the right reasons, with way less stress and uncertainty.
As Daniel said at the end of our conversation: “Stick to your guns. You know more than you think you know. Work with development. Be confident in what you do.”
That’s solid advice.
And having data to back up your decisions? That makes being confident a whole lot easier.
Want to see how Test Impact Analysis works in practice?
Parasoft is offering demos of their solution.
Check it out at parasoft.com or listen to my full conversation with Daniel and Wilhelm for more details on how this plays out in real-world testing scenarios.
And if you’re struggling with regression scope decisions, you’re not alone. This is one of the most common challenges I hear about from the 40,000+ testers in the TestGuild community.
The difference between struggling and succeeding often comes down to having the right data at the right time.
Joe Colantonio is the founder of TestGuild, an industry-leading platform for automation testing and software testing tools. With over 25 years of hands-on experience, he has worked with top enterprise companies, helped develop early test automation tools and frameworks, and runs the largest online automation testing conference, Automation Guild.
Joe is also the author of Automation Awesomeness: 260 Actionable Affirmations To Improve Your QA & Automation Testing Skills and the host of the TestGuild podcast, which he has released weekly since 2014, making it the longest-running podcast dedicated to automation testing. Over the years, he has interviewed top thought leaders in DevOps, AI-driven test automation, and software quality, shaping the conversation in the industry.
With a reach of over 400,000 across his YouTube channel, LinkedIn, email list, and other social channels, Joe’s insights impact thousands of testers and engineers worldwide.
He has worked with some of the top companies in software testing and automation, including Tricentis, Keysight, Applitools, and BrowserStack, as sponsors and partners, helping them connect with the right audience in the automation testing space.
Follow him on LinkedIn or check out more at TestGuild.com.
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