Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2307.12082 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910298628161536 |
|---|---|
| author | Jin, Siyuan Zhang, Mianmian Guo, Yekai He, Yuejiang Li, Ziyuan Chen, Bichao Zhu, Bing Xia, Yong |
| author_facet | Jin, Siyuan Zhang, Mianmian Guo, Yekai He, Yuejiang Li, Ziyuan Chen, Bichao Zhu, Bing Xia, Yong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_12082 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Software Code Quality Measurement: Implications from Metric Distributions Jin, Siyuan Zhang, Mianmian Guo, Yekai He, Yuejiang Li, Ziyuan Chen, Bichao Zhu, Bing Xia, Yong Software Engineering 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. |
| title | Software Code Quality Measurement: Implications from Metric Distributions |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2307.12082 |