tech-debt-tracker

To prioritize technical debt in a legacy codebase, scan your repositories to detect debt signals, compute severity scores, and generate a data-driven remediation plan. This framework surfaces high-priority debts to guide your refactoring decisions and forecast maintenance effort.

Tracking tech debt trends over time requires a dashboard that monitors debt signals across scanning intervals. This provides executives with clear visibility into maintenance costs and code quality, enabling structured planning for cleanup sprints based on quantified severity scores.

Yes, you can scan multiple repositories to identify code quality and maintenance cost signals. The framework applies to software projects of varying sizes, automatically detecting debt signals across your codebase to support comprehensive refactoring prioritization.

Plan cleanup sprints by applying a cost-of-delay framework to analyze and prioritize identified tech debt items. The prioritizer evaluates severity scores from repository scans, allowing you to forecast maintenance effort and sequence refactoring tasks for healthier code.

Yes, tech debt scanning works for aging modules in a monorepo. The tracker identifies debt signals within legacy monorepos, computes severity scores, and outputs a prioritized remediation plan to help you manage maintenance costs and reduce development bottlenecks.

Automated tech debt prioritization should not replace manual review when addressing complex, highly coupled architecture changes. While the scanner provides severity scores and cost-of-delay analysis, critical refactoring decisions still require evaluating business logic impacts beyond detected code signals.