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Root-cause analysis for multi-file software bugs requires collecting observations, formulating competing hypotheses, and mapping supporting or conflicting evidence to reach traceable conclusions. This approach enforces an evidence-driven workflow by structuring logs and configs into hypothesis lanes with falsification plans.

Investigating cross-file architecture problems using evidence involves capturing actual observations and mapping artifacts like logs and file references to support or refute competing explanations. This structured method differentiates potential root causes by quantifying the strength of gathered evidence against each hypothesis.

A hypothesis-driven workflow for runtime performance investigations lets you apply structured evidence gathering to test multiple explanations for observed issues. It enforces disciplined progress through observation collection, evidence mapping, and explicit falsification plans to safely handle uncertain results.

Evidence mapping for diagnosing complex software system failures works by gathering artifacts such as logs and configurations to differentiate explanations and quantify their strength. It connects each piece of evidence directly to supporting or conflicting roles against specific hypotheses.

This structured investigation approach supports parallel hypothesis tracking for teams through defined guardrails and parallel hypothesis lanes. It enables team-mode readiness by allowing multiple explanations to be investigated simultaneously with structured rebuttal rounds and explicit execution policies.

You should not use a falsification plan for root-cause analysis when dealing with single-file syntax errors or isolated issues where the root cause is immediately observable without competing explanations. It is designed for complex cross-file investigations requiring competing hypotheses and evidence mapping.