Entropyx: A Longitudinal Entropy-Based Framework for Measuring Code Drift and Technical Debt in Modern Software Systems
Abstract
Entropyx computes per-file badness using weighted, min-max normalized metrics, including log-scaled source lines of code, complexity density, smells per KLOC, coupling proxies, and maintainability indices. Files with negligible badness (≤ 10⁻⁹) are excluded to reduce floating-point noise. Shannon entropy (base-2) is computed over the distribution of per-file badness values and normalized by log₂(N), where N is the number of non-negligible files. The final Entropyx Score is defined as normalized entropy multiplied by mean badness, capturing both diffusion and magnitude of structural degradation. The framework is language-agnostic and designed for cross-platform CLI implementation.
Fields of Study & Subjects
Software Engineering Research
Software Engineering Techniques and Practices
Software System Performance and Reliability
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 26 February 2026
- Language
- EN
- Document type
- Preprint