Code Red: The Business Impact of Code Quality -- A Quantitative Study of 39 Proprietary Production Codebases
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Abstract (arXiv)
Code quality remains an abstract concept that fails to get traction at the business level. Consequently, software companies keep trading code quality for time-to-market and new features. The resulting technical debt is estimated to waste up to 42% of developers' time. At the same time, there is a global shortage of software developers, meaning that developer productivity is key to software businesses. Our overall mission is to make code quality a business concern, not just a technical aspect. Our first goal is to understand how code quality impacts 1) the number of reported defects, 2) the time to resolve issues, and 3) the predictability of resolving issues on time. We analyze 39 proprietary production codebases from a variety of domains using the CodeScene tool based on a combination of source code analysis, version-control mining, and issue information from Jira. By analyzing activity in 30,737 files, we find that low quality code contains 15 times more defects than high quality code. Furthermore, resolving issues in low quality code takes on average 124% more time in development. Finally, we report that issue resolutions in low quality code involve higher uncertainty manifested as 9 times longer maximum cycle times. This study provides evidence that code quality cannot be dismissed as a technical concern. With 15 times fewer defects, twice the development speed, and substantially more predictable issue resolution times, the business advantage of high quality code should be unmistakably clear.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Mar 2022.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 3; 2026: 3.
8 papers.
- 2026Triage: Routing Software Engineering Tasks to Cost-Effective LLM Tiers via Code Quality Signalscited by 3
- 2026Code for Machines, Not Just Humans: Quantifying AI-Friendliness with Code Health Metricscited by 2
- 2024Trust Calibration in IDEs: Paving the Way for Widespread Adoption of AI Refactoringcited by 1
- 2025UnitTenX: Generating Tests for Legacy Packages with AI Agents Powered by Formal Verificationcited by 1
- 2022evoML Yellow Paper: Evolutionary AI and Optimisation Studiocited by 0
- 2025A Study on the Improvement of Code Generation Quality Using Large Language Models Leveraging Product Documentationcited by 0
- 2025A Note on Code Quality Score: LLMs for Maintainable Large Codebasescited by 0
- 2026SynH-Rank: Quality-Aware Code Search via Diverse Data Synthesis and Hierarchical Ranking Trainingcited by 0
- 2026SynH-Rank: Quality-Aware Code Search via Diverse Data Synthesis and Hierarchical Ranking Training19 Jul 2026
- 2026Triage: Routing Software Engineering Tasks to Cost-Effective LLM Tiers via Code Quality Signals8 Apr 2026
- 2026Code for Machines, Not Just Humans: Quantifying AI-Friendliness with Code Health Metrics5 Jan 2026
- 2025UnitTenX: Generating Tests for Legacy Packages with AI Agents Powered by Formal Verification6 Oct 2025
- 2025A Note on Code Quality Score: LLMs for Maintainable Large Codebases1 Aug 2025
- 2025A Study on the Improvement of Code Generation Quality Using Large Language Models Leveraging Product Documentation22 Mar 2025
- 2024Trust Calibration in IDEs: Paving the Way for Widespread Adoption of AI Refactoring20 Dec 2024
- 2022evoML Yellow Paper: Evolutionary AI and Optimisation Studio20 Dec 2022