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arXiv · 2307.12082

Software Code Quality Measurement: Implications from Metric Distributions

Siyuan Jin·Mianmian Zhang·Yekai Guo·Yuejiang He·Ziyuan Li·Bichao Chen·Bing Zhu·Yong Xia
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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.

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Siyuan Jin, Mianmian Zhang, Yekai Guo, Yuejiang He, Ziyuan Li, Bichao Chen, Bing Zhu, Yong Xia. 2023-07-22. Software Code Quality Measurement: Implications from Metric Distributions. https://doi.org/10.1109/qrs60937.2023.00054

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