# Code Red: The Business Impact of Code Quality -- A Quantitative Study of 39 Proprietary Production Codebases

arXiv: https://arxiv.org/abs/2203.04374 · HTML: https://mlsanity.com/paper/2203.04374/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2022.

## Built on by (most cited first, 8 of 8)

- [Triage: Routing Software Engineering Tasks to Cost-Effective LLM Tiers via Code Quality Signals](https://mlsanity.com/paper/2604.07494/) (2026)
- [Code for Machines, Not Just Humans: Quantifying AI-Friendliness with Code Health Metrics](https://mlsanity.com/paper/2601.02200/) (2026)
- [Trust Calibration in IDEs: Paving the Way for Widespread Adoption of AI Refactoring](https://mlsanity.com/paper/2412.15948/) (2024)
- [UnitTenX: Generating Tests for Legacy Packages with AI Agents Powered by Formal Verification](https://mlsanity.com/paper/2510.05441/) (2025)
- [evoML Yellow Paper: Evolutionary AI and Optimisation Studio](https://mlsanity.com/paper/2212.10671/) (2022)
- [A Study on the Improvement of Code Generation Quality Using Large Language Models Leveraging Product Documentation](https://mlsanity.com/paper/2503.17837/) (2025)
- [A Note on Code Quality Score: LLMs for Maintainable Large Codebases](https://mlsanity.com/paper/2508.02732/) (2025)
- [SynH-Rank: Quality-Aware Code Search via Diverse Data Synthesis and Hierarchical Ranking Training](https://mlsanity.com/paper/2607.17139/) (2026)

## 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.