Flaky Tests, Fragile Trust: How AI Can Restore Confidence in Test Automation – part 1

By Eduard Dubilyer , CTO & Automation Testing Expert at Skipper Soft

“Yeah, the tests failed again… but it’s probably nothing.”

I heard that during a client’s sprint review years ago—and I’ve heard it dozens of times since.

That phrase says more about a team’s quality culture than any coverage report or testing dashboard ever could.

Because it’s not just about the failed test—it’s about fragile trust in the entire automation suite.

The Silent Saboteur: Test Flakiness

At Skipper Soft, we work with fast-growing startups, teams that move quickly and often lack dedicated QA staff.

And in that environment, flaky tests hit harder than most people expect:

• CI pipelines become unreliable.
• Developers waste hours rerunning tests to “prove it’s not their bug.”
• Teams start to tune out failures—“It always fails once.”
• Releases get delayed or, worse, shipped with hidden bugs.

Eventually, someone admits what everyone’s been thinking:

“We have tests, but we can’t trust them.”

What We Did Before AI

Back when I led my first test automation project, managing flaky tests meant:

• Sorting through logs from the last 10 builds manually.
• Moving unstable tests to a quarantine folder.
• Writing scripts to analyze failure frequency.
• Tracking flaky cases in spreadsheets.

It worked—but only if you had time, discipline, and a whole QA team.

Startups? They don’t get that luxury.

The Shift: AI Isn’t Just a Buzzword, It’s a Breakthrough

Today, I tell every engineering lead we work with:

“You no longer need to choose between speed and stability.”

Thanks to GenAI and machine learning tools, we can now:

• Detect flaky tests across CI history automatically
• Cluster failures by patterns (e.g., async timing, environment drift)
• Predict test flakiness before it disrupts a release

We’ve helped teams integrate these tools directly into their GitHub Actions, CircleCI, or Jenkins pipelines. The result?

Confidence returns. Test quality improves. Release velocity stabilizes.

But AI Isn’t a Free Pass

Here’s the danger:

• Relying on AI to label tests as flaky without validation.
• Using “quarantine” as a permanent solution.
• Believing AI will “handle it” instead of owning the problem.

AI augments your process—it doesn’t replace it. You still need engineering insight and process discipline.

What We Recommend First

Here’s where we start when helping teams rebuild trust in automation:

1. Integrate a test flakiness tracker into your CI (more on that soon).
2. Expose flaky tests visibly—don’t hide them in logs or Slack threads.
3. Assign ownership—unstable tests need owners, not just labels.
4. Fix one flaky test per sprint—confidence is cumulative.
5. Track stability as a KPI—next to coverage, it’s your second-most important metric.

The Bottom Line

Flaky tests aren’t just annoying. They undermine your test strategy, release process, and team’s trust in automation. But with the right tools—and the right mindset—you can reverse that erosion.

Start by fixing visibility.

Let AI surface the patterns.

Then, take action.

What’s Next

In the following article, I’ll break down:

• The top flakiness tracking tools (like BuildPulse, Launchable, CircleCI Insights)
• How to use them effectively
• What best practices do we apply across clients