Research Basis
CodeDecay is motivated by research on software evolution, pull request impact, and AI-era code quality risks.
SlopCodeBench
SlopCodeBench studies how coding agents degrade over long-horizon iterative tasks. It tracks verbosity and structural erosion, including duplicated code and complexity concentration in high-complexity functions.
Reference: https://arxiv.org/html/2603.24755v1
Does Code Decay?
“Does Code Decay? Assessing the Evidence from Change Management Data” connects software evolution data to code decay and maintenance risk.
References:
- https://www.niss.org/sites/default/files/technicalreports/tr81.pdf
- https://dl.acm.org/doi/10.1109/32.895984
Pull Request Change Impact
Pull request change impact research supports using code structure and changed artifact relationships to improve review focus and risk awareness.
Reference: https://link.springer.com/article/10.1007/s10664-024-10600-2
AI Code Quality, Churn, And Duplication
AI-assisted development can increase code volume, churn, and duplicated code when teams optimize only for immediate output. CodeDecay turns those concerns into local PR checks.
Reference: https://www.gitclear.com/ai_assistant_code_quality_2025_research