Diagnose

To reproduce a software bug, you can build a deterministic feedback loop using tests, curl/HTTP, CLI snapshots, headless browsers, or trace replay until you get a reliable, repeatable signal confirming the reported failure.

A falsifiable hypothesis in debugging is a prioritized, testable cause-and-effect statement with explicit predictions. It guides your diagnosis by verifying or eliminating suspected causes through probe-gated execution before implementation.

You create a test harness by selecting a repro strategy like fuzzing or bisecting and driving it until the loop is debuggable at a high enough rate for flaky bugs, capturing the exact symptom needed for later verification.

The best way to diagnose bugs without production changes is to create a diagnosis probe using available test, API, UI, trace, or harness techniques to gather evidence and generate ranked hypotheses for subsequent execution.

Yes, you can use trace replay and differential runs as repro strategies to drive a deterministic feedback loop, verifying that the loop reproduces the user-described failure and captures the exact symptom for verification.

You should not use a diagnosis probe approach if your work type is not a bug, or if you cannot establish a repeatable feedback loop, as the method requires symptom capture and repeatability checks to generate falsifiable hypotheses.