  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.
