首页|Supporting the identification of prevalent quality issues in code changes by analyzing reviewers' feedback
Supporting the identification of prevalent quality issues in code changes by analyzing reviewers' feedback
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Springer Nature
Context: Code reviewers provide valuable feedback during the code review. Identifying common issues described in the reviewers' feedback can provide input for devising context-specific software development improvements. However, the use of reviewer feedback for this purpose is currently less explored. Objective: In this study, we assess how automation can derive more interpretable and informative themes in reviewers' feedback and whether these themes help to identify recurring quality-related issues in code changes. Method: We conducted a participatory case study using the JabRef system to analyze reviewers' feedback on merged and abandoned code changes. We used two promising topic modeling methods (GSDMM and BERTopic) to identify themes in 5,560 code review comments. The resulting themes were analyzed and named by a domain expert from JabRef. Results: The domain expert considered the identified themes from the two topic models to represent quality-related issues. Different quality issues are pointed out in code reviews for merged and abandoned code changes. While BERTopic provides higher objective coherence, the domain expert considered themes from short-text topic modeling more informative and easy to interpret than BERTopic-based topic modeling. Conclusions: The identified prevalent code quality issues aim to address the maintainability-focused issues. The analysis of code review comments can enhance the current practices for JabRef by improving the guidelines for new developers and focusing discussions in the developer forums. The topic model choice impacts the inter-pretability of the generated themes, and a higher coherence (based on objective measures) of generated topics did not lead to improved interpretability by a domain expert.
Modern code reviewSoftware quality improvementNatural language processingOpen-source systems
Umar Iftikhar、Juergen Boerstler、Nauman Bin Ali、Oliver Kopp
展开 >
Blekinge Institute of Technology, Valhallavaegen 1, SE-37179 Karlskrona, Blekinge, Sweden
University of Stuttgart, Universitaetsstr. 38, 70593 Stuttgart, Baden-Wuerttemberg, Germany
NETL
NSTL
Springer Nature
