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.

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