Graph-Based Salient Class Classification in Commits

In software engineering, code review is an important process when a project is to be upgraded. Reviewers need to assess the validity of a commit, even if they are not familiar with the files in the commit. In a typical commit, one or more mainly modified classes referred to as salient classes, may cause modifications in other classes. Salient Class Identification is such a method that can help reviewers review commits more effectively. In this way, after identifying salient classes, reviewers can allocate most of their efforts to analyzing the salient class, comprehending the commit, and providing reasoned assessments. The existing Salient Class Identification model is based on the static features of the code and does not analyze the internal logical information, such as the relationships between statements. We thoroughly consider both internal and external code information in commits, using a detailed Code-Change Dependency Graph (CCDG) to depict the code structure. CCDG includes various node and edge types, supporting complex syntax scenarios, which can capture fine-grained dependencies. Finally, based on a heterogeneous graph neural network, we extract nuanced features embedding from CCDG, which can further boost the performance of our model. The experiment result shows that our model outperforms existing models in Salient Class Identification, achieving an overall $88 \%$ accuracy.