Abstract
Source code quality plays a key role in software quality mainly due to its impact on software maintainability. Software engineers have been using source code metrics to support them to assess source code quality. Source code metrics quantify different source code characteristics. However, source code metric analysis still involves subjectivity. For instance, it is not trivial to decide whether a metric value is high or low. To reduce the eventual subjectivity of source code metrics analysis, several researchers are using Machine Learning algorithms. Therefore, in this paper, we designed a Fuzzy-based approach to extract characteristics and patterns present in source code versioning repositories in order to: i) assist the specialist in the interpretation of releases, especially when working with large volumes of source code; ii) from the release interpretation, specialists can improve the quality of the source code; and iii) monitor the evolution of the software as new releases are submitted to the repositories. We evaluated the proposed approach with the Linux Test Project repository, emphasizing the interpretability of large source code versioning repositories.
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