PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing
Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs.
Wed 25 JunDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
14:00 - 15:20 | LLM for SE 4Research Papers / Journal First at Cosmos Hall Chair(s): Ting Su East China Normal University | ||
14:00 20mTalk | Large Language Models for Software Engineering: A Systematic Literature Review Journal First Xinyi Hou Huazhong University of Science and Technology, Yanjie Zhao Huazhong University of Science and Technology, Yue Liu Monash University, Zhou Yang Singapore Management University; University of Alberta, Kailong Wang Huazhong University of Science and Technology, Li Li Beihang University, Xiapu Luo Hong Kong Polytechnic University, David Lo Singapore Management University, John Grundy Monash University, Haoyu Wang Huazhong University of Science and Technology | ||
14:20 20mTalk | Calibration of Large Language Models on Code Summarization Research Papers DOI | ||
14:40 20mTalk | Code Red! On the Harmfulness of Applying Off-the-shelf Large Language Models to Programming Tasks Research Papers Ali Al-Kaswan Delft University of Technology, Netherlands, Sebastian Deatc Delft University of Technology, Begüm Koç Delft University of Technology, Arie van Deursen TU Delft, Maliheh Izadi Delft University of Technology DOI Pre-print | ||
15:00 20mTalk | PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing Journal First Yuwei Zhang Institute of Software Chinese Academy of Sciences, Zhi Jin Peking University, xingying Beijing University of Posts and Telecommunications, Ge Li Peking University, Fang Liu Beihang University, Jiaxin Zhu Institute of Software at Chinese Academy of Sciences, Wensheng Dou Institute of Software Chinese Academy of Sciences, Jun Wei Institute of Software at Chinese Academy of Sciences; University of Chinese Academy of Sciences | ||
This is the main event hall of Clarion Hotel, which will be used to host keynote talks and other plenary sessions. The FSE and ISSTA banquets will also happen in this room.
The room is just in front of the registration desk, on the other side of the main conference area. The large doors with numbers “1” and “2” provide access to the Cosmos Hall.