# Ghost Echoes Revealed: Benchmarking Maintainability Metrics and Machine Learning Predictions Against Human Assessments

arXiv: https://arxiv.org/abs/2408.10754 · HTML: https://mlsanity.com/paper/2408.10754/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2024.

## Built on by (most cited first, 2 of 2)

- [Code for Machines, Not Just Humans: Quantifying AI-Friendliness with Code Health Metrics](https://mlsanity.com/paper/2601.02200/) (2026)
- [LintEval: Benchmarking LLMs on Linter-Guided File Refactoring](https://mlsanity.com/paper/2610.20027/) (2026)

## Abstract (arXiv)

As generative AI is expected to increase global code volumes, the importance of maintainability from a human perspective will become even greater. Various methods have been developed to identify the most important maintainability issues, including aggregated metrics and advanced Machine Learning (ML) models. This study benchmarks several maintainability prediction approaches, including State-of-the-Art (SotA) ML, SonarQube's Maintainability Rating, CodeScene's Code Health, and Microsoft's Maintainability Index. Our results indicate that CodeScene matches the accuracy of SotA ML and outperforms the average human expert. Importantly, unlike SotA ML, CodeScene also provides end users with actionable code smell details to remedy identified issues. Finally, caution is advised with SonarQube due to its tendency to generate many false positives. Unfortunately, our findings call into question the validity of previous studies that solely relied on SonarQube output for establishing ground truth labels. To improve reliability in future maintainability and technical debt studies, we recommend employing more accurate metrics. Moreover, reevaluating previous findings with Code Health would mitigate this revealed validity threat.