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Dataset of Commit Classification via Diff-Code GCN based on System Dependency Graph

<p>Commit Classification via Diff-Code GCN based on System Dependency Graph</p> <p>The&nbsp;dataset&nbsp;is based on Lobna Ghadhab et al. [1]. Levin et al.[2]&#39;s dataset, and we extract all commits with pure java codes of two versions.&nbsp;</p> <p>In the dataset, evert commit folder have two sub-folder called before and after, they contains two version of codes. we extracted it by pydriller.</p> <p>The dataset have 1213 commits with two version java codes,and it contains three categories:</p> <p>(1) The first category is Corrective, which involves rectifying errors and faults identified during software usage.</p> <p>(2)The second category is Perfective, which entails enhancing software quality attributes, such as performance, maintainability, and usability.</p> <p>(3) Lastly, is Adaptive, which&nbsp;encompasses adapting the software to new environments (e.g., software or hardware) or introducing new functionalities.</p> <p>The dataset have 450 labels of&nbsp;Corrective. 441 for Perfective the rest for&nbsp;Adaptive.</p> <p>&nbsp;</p> <p>[1]L. Ghadhab, I. Jenhani, M. W. Mkaouer, and M.Ben Messaoud, &rdquo;Augmenting commit classification by using fine-grained source code changes and a pretrained deep neural language model,&rdquo; Information and Software Technology, vol. 135, p. 106566, 2021/07/01/2021.</p> <p>[2]S. Levin and A. Yehudai, &rdquo;Using Temporal and Semantic Developer-Level Information to Predict Main</p> <p>tenance Activity Profiles,&rdquo; in 2016 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2016.</p> <p>&nbsp;</p>