First-pass root-cause hypotheses
Have the model propose and rank possible causes for a failure — from the error, the recent changes and the code involved — along with the quickest check that would confirm or rule out each one.
How it works
Investigation goes faster with a short list of candidate explanations than with a blank page. The model is given the failure details, the diff since the last passing run, and the relevant code, and asked for hypotheses ranked by likelihood. The important part is the second half of the prompt: for each hypothesis, what is the cheapest experiment that would distinguish it — rerun on the previous build, check one config value, look at one table. That keeps the output testable rather than speculative. Each hypothesis that is ruled out gets fed back so the next round is better informed.
When to use it
Failures with no obvious cause; failures in code the investigator doesn't know well; regressions after large merges; and when developers are unavailable and testers need to narrow things down before handing over.
Limitations
The model favours causes it can see in the material given — usually the most recent change — and can anchor the investigation on it. Environment, data and timing problems are underweighted because they leave little trace in code. Its ranking reflects plausibility of the story, not probability, and a confident first hypothesis tends to be checked first whether or not it deserves to be.
Watch for
A rerun on the previous build passes, so the latest change is blamed and reverted, but the failure was intermittent and comes back. Repeat a deciding experiment enough times to rule out chance before accepting the verdict.
What you still own
Running the experiments, keeping alternative explanations alive until the evidence rules them out, and the final diagnosis.
Cite this
Qlarify Labs. (2026). First-pass root-cause hypotheses. Retrieved from https://labs.qlarify.fi/ai-in-testing/first-pass-root-cause-hypotheses