Code Red: The Business Impact of Code Quality
summary
Large-scale measurements tie code quality to business outcomes: automated analysis of Git and Jira data across 39 companies and 14 languages uses CodeScene visualizations and hotspot x‑rays to map file-level code health and change frequency. Results show feature work in red modules is on average 2x slower, can take an order of magnitude longer unpredictably, and red files carry about 15x more defects. Practical fix: focus on hotspots (2–3% of files) and prioritize refactors where change frequency and complexity metrics concentrate risk.
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chapters (8)
- 0:00Intro
- 0:20What is technical debt?
- 5:23Visualize technical debt & code complexity
- 14:46Quantify the business impact of code quality
- 19:02Does code quality matter?
- 28:13Prioritize remediation to large amounts of red code
- 39:07Resources
- 40:25Q&A
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