investigate

Root cause analysis for production bugs and performance regressions requires structured evidence collection across code paths, logs, and tests to confirm a causal chain. This approach generates ranked hypotheses and tests them against gathered data.

Debugging unexpected behavior with log analysis involves reviewing errors and external factors in parallel to isolate the fault. It moves from confusing symptoms to a confirmed diagnosis by validating multiple hypotheses against structured evidence.

Incident response and code investigation benefit from parallel hypothesis generation and test reproduction to validate potential causes. This multi-angle evidence collection reviews code paths and test behavior to produce a detailed report with remediation guidance.

Hypothesis-driven debugging applies to complex scenarios by evaluating external environmental factors alongside code and logs. It tests generated hypotheses against multi-angle evidence to deliver an evidence-backed report with a confidence level and causal chain.

Code investigation generates prioritized prevention steps after confirming a root cause through evidence-backed hypothesis testing. The process outputs a detailed report containing concrete recommendations, confidence scoring, and passing tests to prevent future regressions.

Root cause analysis for failing tests requires providing an issue description, error messages, and relevant stack traces to begin investigation. This input enables parallel hypothesis generation and structured evidence collection across code and logs.