AI test analytics
Every automated suite generates a lot of data and almost none of it gets read. Pass rates, durations, screenshots, logs, error strings, run after run after run. Traditional reporting turns that into dashboards, which answers "how many failed" but not the question anyone actually has on a Monday morning, which is "what is broken and what do I do about it."
Analytics closes that gap by looking across runs instead of at one. Forty failures spread over twelve tests are frequently one root cause, and a model comparing error text, failed steps, and the state of the application at each break can say so, collapsing the list into a single item with twelve symptoms. It can also tell a test that fails every time from a test that fails one run in nine, which is the distinction between a bug and flakiness, and one that a pass rate alone will never surface.
Then there's the longer view. Suites decay slowly and quietly, and the pattern only appears in aggregate: a group of tests that has needed repairs on each of the last six releases, a flow whose runtime has crept up 40 percent since spring, a set of cases that has passed for a year without ever catching anything. None of that shows up in a single run report. It's the kind of thing that turns test data into an argument for where to spend the next sprint.
What it produces
- Failure clustering, grouping many broken tests under one likely root cause instead of listing them separately.
- Flakiness scoring, ranking tests by how inconsistently they behave across otherwise identical runs.
- Plain-language failure summaries, reading logs and step traces into an explanation rather than a stack trace.
- Coverage and redundancy signals, showing which flows are untested and which cases duplicate work others already do.
- Trends over time, tracking pass rate, duration, and repair frequency so decay is visible before it's painful.
- Risk views for a release, summarizing what's failing in terms of the flows it affects, not the tests it names.
Where you'll see it
- Morning triage on a nightly run, where the useful output is five root causes rather than ninety failed tests.
- Release readiness calls, where someone has to say whether the failures still open are blocking.
- Suite cleanup, finding the tests that are consistently flaky or have never caught a defect worth keeping.
- Feeding the ranking that decides what runs first, since failure history and defect yield come out of exactly this data.
Benefits and challenges
| Benefit | Challenge |
|---|---|
| Triage starts from a handful of causes instead of a wall of red. | Clustering can merge two genuinely separate bugs that happen to fail the same way, so the grouping needs a look. |
| Flaky tests become visible and measurable rather than a thing people complain about. | Reliable flakiness scores need a decent volume of runs, so a young suite gives thin answers. |
| Suite decay shows up as a trend line while it's still cheap to fix. | Trends only mean something if run history is stored consistently, and CI setups are rarely tidy about that. |
| QA gets numbers to bring to a planning conversation instead of anecdotes. | A confident summary of a failure is still an inference. It points at a cause, it doesn't prove one. |
Frequently asked questions
How is this different from a test reporting dashboard?
A dashboard shows what happened: counts, durations, pass rates. Analytics interprets it, deciding which failures share a cause, which are flaky, and which trends are worth reacting to. The dashboard is the input, not the answer.
Can it tell a flaky test from a real bug?
Usually, given enough runs. A test failing intermittently on unchanged code looks very different from one failing consistently after a specific commit. What it can't do is tell you whether the flakiness comes from the test or from a genuine race condition in the product, and those need different fixes.
Do we need a large suite for this to be worth it?
The value scales with volume. On thirty tests you can read every failure yourself. Past a few hundred running daily, nobody does, and that's the point where clustering and flakiness scoring start saving real hours.
How Klarent helps
Klarent records each run with the steps taken and what the agent saw at every one, so a failure can be analyzed against what actually happened rather than reconstructed from a screenshot and a log file.