Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5

• URL: http://arxiv.org/abs/2211.14875v2
• Date: Thu, 1 Dec 2022 17:28:40 GMT
• Title: Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5
• Authors: Nghi D. Q. Bui, Yue Wang, Steven Hoi
• Abstract summary: We propose a novel unified emphDetect-Localize-Repair framework based on a pretrained programming language model CodeT5. Our model significantly outperforms existing baselines from both NLP and software engineering domains.
• Score: 14.712753336831172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated software debugging is a crucial task for improving the productivity of software developers. Many neural-based techniques have been proven effective for debugging-related tasks such as bug localization and program repair (or bug fixing). However, these techniques often focus only on either one of them or approach them in a stage-wise manner, ignoring the mutual benefits between them. In this work, we propose a novel unified \emph{Detect-Localize-Repair} framework based on a pretrained programming language model CodeT5 to seamlessly address these tasks, named CodeT5-DLR. Specifically, we propose three objectives to adapt the generic CodeT5 for debugging: a bug detection objective to determine whether a given code snippet is buggy or not, a bug localization objective to identify the buggy lines, and a program repair objective to translate the buggy code to its fixed version. We evaluate it on each of these tasks and their combined setting on two newly collected line-level debugging datasets in Java and Python. Extensive results show that our model significantly outperforms existing baselines from both NLP and software engineering domains.

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