This paper introduces DSrepair, a knowledge-enhanced program repair method designed to repair the buggy code generated by LLMs in the data science domain. DSrepair uses knowledge graph based RAG for API knowledge retrieval as well as bug knowledge enrichment to construct repair prompts for LLMs. Specifically, to enable knowledge graph based API retrieval, we construct DS-KG (Data Science Knowledge Graph) for widely used data science libraries. For bug knowledge enrichment, we employ an abstract syntax tree (AST) to localize errors at the AST node level. DSrepair’s effectiveness is evaluated against five state-of-the-art LLM-based repair baselines using four advanced LLMs on the DS-1000 dataset. The results show that DSrepair surpasses all five baselines. Specifically, when compared to the second-best baseline, DSrepair demonstrates significant improvements, fixing 44.4%, 14.2%, 20.6%, and 32.1% more buggy code snippets for each of the four evaluated LLMs, respectively. Additionally, it achieves greater efficiency, reducing the number of tokens required per code task by 17.49%, 34.24%, 24.71%, and 17.59%, respectively.
Wed 30 AprDisplayed time zone: Eastern Time (US & Canada) change
16:00 - 17:30 | AI for Analysis 2Research Track / Journal-first Papers at 212 Chair(s): Julia Rubin The University of British Columbia | ||
16:00 15mTalk | Neurosymbolic Modular Refinement Type Inference Research Track Georgios Sakkas UC San Diego, Pratyush Sahu UC San Diego, Kyeling Ong University of California, San Diego, Ranjit Jhala University of California at San Diego | ||
16:15 15mTalk | An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We? Research Track Hyunjae Suh University of California, Irvine, Mahan Tafreshipour University of California at Irvine, Jiawei Li University of California Irvine, Adithya Bhattiprolu University of California, Irvine, Iftekhar Ahmed University of California at Irvine | ||
16:30 15mTalk | Planning a Large Language Model for Static Detection of Runtime Errors in Code Snippets Research Track Smit Soneshbhai Patel University of Texas at Dallas, Aashish Yadavally University of Texas at Dallas, Hridya Dhulipala University of Texas at Dallas, Tien N. Nguyen University of Texas at Dallas | ||
16:45 15mTalk | LLMs Meet Library Evolution: Evaluating Deprecated API Usage in LLM-based Code Completion Research Track Chong Wang Nanyang Technological University, Kaifeng Huang Tongji University, Jian Zhang Nanyang Technological University, Yebo Feng Nanyang Technological University, Lyuye Zhang Nanyang Technological University, Yang Liu Nanyang Technological University, Xin Peng Fudan University | ||
17:00 15mTalk | Knowledge-Enhanced Program Repair for Data Science Code Research Track Shuyin Ouyang King's College London, Jie M. Zhang King's College London, Zeyu Sun Institute of Software, Chinese Academy of Sciences, Albert Merono Penuela King's College London | ||
17:15 7mTalk | SparseCoder: Advancing Source Code Analysis with Sparse Attention and Learned Token Pruning Journal-first Papers Xueqi Yang North Carolina State University, Mariusz Jakubowski Microsoft, Li Kang Microsoft, Haojie Yu Microsoft, Tim Menzies North Carolina State University Link to publication DOI | ||