Software Code Quality Measurement: Implications from Metric Distributions
Abstract
Software code quality is a construct with three dimensions: maintainability, reliability, and functionality. Although many firms have incorporated code quality metrics in their operations, evaluating these metrics still lacks consistent standards. We categorized distinct metrics into two types: 1) monotonic metrics that consistently influence code quality; and 2) non-monotonic metrics that lack a consistent relationship with code quality. To consistently evaluate them, we proposed a distribution-based method to get metric scores. Our empirical analysis includes 36,460 high-quality open-source software (OSS) repositories and their raw metrics from SonarQube and CK. The evaluated scores demonstrate great explainability on software adoption. Our work contributes to the multi-dimensional construct of code quality and its metric measurements, which provides practical implications for consistent measurements on both monotonic and non-monotonic metrics.
Cite
@article{arxiv.2307.12082,
title = {Software Code Quality Measurement: Implications from Metric Distributions},
author = {Siyuan Jin and Mianmian Zhang and Yekai Guo and Yuejiang He and Ziyuan Li and Bichao Chen and Bing Zhu and Yong Xia},
journal= {arXiv preprint arXiv:2307.12082},
year = {2024}
}
Comments
The paper has been accepted for presentation at IEEE QRS 2023. Unfortunately, due to authorship limits, Mianmian Zhang, Yekai Guo, and Yuejiang He could not be included as co-authors. However, we gratefully acknowledge their valuable contributions to this work and use this arXiv version to prove their contributions