Just-In- Time defect prediction models can identify defect-inducing commits at check-in time and many approaches are proposed with remarkable performance. However, these approaches still have a few limitations which affect their effectiveness and practical usage: (1) partially using semantic information or structure information of code, (2) coarsely providing results to a commit (buggy or clean), and (3) independently investigating the defect prediction model and defect repair model. In this study, to handle the aforementioned limitations, we propose a unified defect prediction and repair framework named COMPDEFECT,which can identify whether a changed function inside a commit is defect-prone, categorize the type of defect, and repair such a defect automatically if it falls into several scenarios, e.g., defects with single statement fixes, or those that match a small set of defect templates. Technically, the first two tasks in COMPDEFECT are treated as a multiclass classification task, while the last task is treated as a sequence generation task. To verify the effectiveness of COMPDEFECT, we first build a large-scale function-level dataset (i.e., 21,047) named Function-SStuBs4J and then compare COMPDEFECT with tens of state-of-the-art (SOTA) approaches by considering five performance measures. The experimental results indicate that COMPDEFECT outperforms all SOTAs with a substantial improvement in three tasks separately. Moreover, the pipeline experimental results also indicate the feasibility of COMPDEFECT to unify three tasks in a model.

@inproceedings{NiYZCY23,
  title = {Unifying Defect Prediction, Categorization, and Repair by Multi-Task Deep Learning},
  author = {Chao Ni and Kaiwen Yang and Yan Zhu and Xiang Chen and Xiaohu Yang},
  booktitle = {ASE},
  year = {2023},
  doi = {10.1109/ase56229.2023.00083}
}