It builds up gradually until one day your team can barely ship anything
Average codebase accumulates $1M in tech debt per 100k lines of code. Interest compounds at 25% annually. Most teams don't notice until velocity drops 50%.
Teams lose 23% of development capacity to tech debt. Features that took 2 days now take 2 weeks. Engineers spend 33% of time on maintenance instead of building.
78% of teams can't quantify their tech debt. Without metrics, it's impossible to prioritize. The average hidden tech debt is 5x what teams estimate.
Only 15% of planned refactoring actually happens. Tech debt cleanup gets deprioritized in 85% of sprints. Each quarter of delay increases fix cost by 20%.
Comprehensive tech debt analysis on every pull request
Files and functions that have grown too complex. High cyclomatic complexity, deep nesting, too many dependencies.
Cyclomatic ComplexityCopy-paste patterns that should be abstracted. DRY violations that multiply maintenance burden.
Refactoring GuruUnused functions, unreachable branches, deprecated paths. Code that exists but serves no purpose.
Refactoring GuruModules that depend on each other too tightly. Changes in one place break things everywhere.
Martin FowlerLegacy approaches that should be modernized. Old APIs, deprecated libraries, dated practices.
Repeated logic that should be extracted. Opportunities to simplify and consolidate.
Catch tech debt as it's introduced, not after it compounds
Every PR is analyzed for new tech debt introduction
AI identifies complexity, duplication, and coupling issues
Prioritization based on severity and affected areas
Specific recommendations with suggested refactoring paths
Every piece of tech debt caught at PR time saves hours of future debugging, refactoring, and firefighting. diffray makes prevention automatic.
"We had no idea how much tech debt we were introducing. diffray showed us we were adding 15% complexity per quarter. Now we catch it before merge."
Mike Torres
Engineering Director, E-commerce Platform
"The refactoring suggestions aren't just 'this is bad' — they're actual paths forward with estimated impact. That makes prioritization possible."
Priya Sharma
Staff Engineer, Healthcare Tech
diffray analyzes code structure, complexity metrics, duplication patterns, and coupling between modules. It uses AI to understand context — distinguishing intentional complexity from problematic debt.
Yes. Findings are prioritized by severity, frequency of changes to affected code, and potential impact. High-churn, high-complexity areas get flagged as top priorities.
The PR-level analysis prevents new debt. For historical tracking, we recommend pairing with your existing metrics. Future updates will include trend dashboards.
Traditional tools give you numbers. diffray gives you context. A function with 100 lines might be fine if it's simple; a function with 20 lines might be terrible if it has 10 side effects. AI understands the difference.
Catch technical debt before it compounds. Free for 14 days, no credit card required.