Defect Identification, Categorization, and Repair: Better Together
Chao Ni, Kaiwen Yang, Xin Xia, David Lo, Xiang Chen, Xiaohu Yang

TL;DR
This paper introduces CompDefect, a comprehensive framework that simultaneously predicts, categorizes, and repairs code defects at a fine-grained function level, addressing limitations of prior defect prediction models.
Contribution
It presents a novel integrated approach combining defect identification, categorization, and automatic repair using multi-task learning on code sequences.
Findings
Effective defect categorization and repair demonstrated
Outperforms existing defect prediction models
Automates defect handling at function level
Abstract
Just-In-Time defect prediction (JIT-DP) models can identify defect-inducing commits at check-in time. Even though previous studies have achieved a great progress, these studies still have the following limitations: 1) useful information (e.g., semantic information and structure information) are not fully used; 2) existing work can only predict a commit as buggy one or clean one without more information about what type of defect it is; 3) a commit may involve changes in many files, which cause difficulty in locating the defect; 4) prior studies treat defect identification and defect repair as separate tasks, none aims to handle both tasks simultaneously. In this paper, to handle aforementioned limitations, we propose a comprehensive defect prediction and repair framework named CompDefect, which can identify whether a changed function (a more fine-grained level) is defect-prone,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSoftware Engineering Research · Software System Performance and Reliability · Software Reliability and Analysis Research
