AI & ML
Evaluation of Software Quality Through Refactoring Commit-Impact Analysis
arXiv cs.SE0 views
arXiv:2610.12890v1 Announce Type: new Abstract: The aim of this study is to conduct an empirical analysis of the correlation between code modifications and software quality. This will be achieved by examining the effects of changes made before and after each refactoring commit, using software metrics such as size, complexity, code smells, vulnerability, and bug detection. The objective is to conduct static code analysis on open-source software from GitHub repositories using various repository mining and static code analysis tools. Our approach involves formulating research-based questions and utilizing the specified tools to obtain results that will enable us to resolve the case study. The obtained results have been summarised and further analyzed using tables and graphs to achieve the objective.
Source:https://arxiv.org/abs/2610.12890PreviousCo-Modifying Application Code and Deployment Infrastructure: A Causal Study of AI Agent Pull RequestsNextSCCGBench: Executability-Oriented Evaluation for Real-World Web Service-Call Code Generation
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