I’ve been in QA for over 12 years, working with Selenium, Cypress, and even Playwright. No matter the framework, one thing never changed: flaky tests.
You might know this if you have worked with these automation frameworks.
Run a test: it fails.
Run it again: it passes.
2 hours later, everything looks fine, and nobody knows what happened.
we tried better locators, smarter waits, retries, reruns, even rebuilding frameworks. But nothing fixed the core problem: is this failure real or just noise?
I felt stuck. My team felt stuck. And honestly, I know thousands of other QA engineers, developers, and managers have been in the same place.
Why Flaky Tests Hurt So Much
- QA engineers spend hours trying to decide if failures are real or flaky.
- Developers ask, “is this a real bug?”
- Product managers can’t tell if results matter or are just noise.
The end result: automated tests lose credibility. Teams start ignoring failures instead of fixing them.
For us, this added up to 6–8 hours every week just debating results.
How TestDino Started
We didn’t set out to build a product. We built TestDino because we needed it ourselves.
Every morning, the same painful ritual:
- Open CI logs
- Compare yesterday’s runs
- Guess if it’s flaky or not
- Ping a developer, wait for a reply
- Repeat for each failure
We were spending more time managing test results than writing tests.
So we built a small tool that classified failures automatically. Just three categories: Bug, Flaky, UI Change.
The impact was immediate.
- What took 2 hours now took 10 minutes.
- Developers stopped asking “is this real?” because the answer was right there.
- Other teams wanted to use it too. That’s when we realized it wasn’t just our problem.
Following is one of the analytics screen in testdino
Why Each Feature Exists
AI Classification
Instead of “Test failed,” TestDino says:
- Actual Bug- 92% confidence
- Flaky Test- 87% confidence
- UI Change- 95% confidence
That clarity saves hours. Ownership is obvious. No confusion.
Git-Aware Intelligence
Every failure links to the exact PR and branch. Example: LoginTest failed after PR #234 changed the auth flow.
We also post summaries to Slack. The person who broke it finds out right away.
Single Source of Truth
Before TestDino, results were everywhere — CI logs, spreadsheets, Slack threads.
Now everything is in one place. You can:
- Filter by committer (see what Bob broke today).
- Filter by environment (check staging stability).
- Filter by duration (see if deploys slowed things down).
Analytics That Show Patterns
Flaky tests often have hidden patterns:
- Failures spike on Mondays (server load).
- Failures after specific deploys (memory leaks).
- Failures in sequence (race conditions).
TestDino reveals these trends. Suddenly, the noise has meaning.
Speed Distribution
We don’t just show average duration. We show full distribution.
If a login test usually takes 2s but sometimes takes 30s, an “average” of 4s hides the real problem. Distribution tells you the truth.
The Real Impact
Here’s what my own team told me:
We stopped hating test failures.
Why? Because they could finally trust the results again.
Tests should catch problems, not cause debates. Debugging should take minutes, not hours.
That’s the point of TestDino: bring trust back to automated testing.
Want Early Access?
We’re now selecting a few Playwright teams for our free beta with lifetime pricing benefits.
If you’re interested, comment “TestDino” and I’ll reach out with details.
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