techdebt

To identify technical debt hotspots in a specific subsystem, target a concrete directory like src/auth to build a file inventory, compute churn signals, and apply categorized patterns to surface actionable findings.

Technical debt audits detect categorized patterns including code smells, duplication, prop drilling, dead code, and architectural smells, applying disqualification filters to reduce noise and produce high-confidence findings.

Run a subsystem audit by targeting a concrete architectural component, building a file inventory, computing churn signals, and evaluating findings with severity to produce a structured report for prioritized remediation.

Yes, you can scan a targeted directory while minimizing false positives by applying disqualification filters during the pattern evaluation phase to ensure only high-confidence findings are emitted.

The best way to prioritize technical debt remediation is to compute churn signals and evaluate debt patterns with severity levels, generating a structured findings report that highlights the most critical architectural smells.

Static analysis for technical debt requires loading defined patterns, such as A–E tech debt patterns, before applying them to the subsystem inventory to ensure findings are evaluated against established architectural criteria.