MissConf: LLM-Enhanced Reproduction of Configuration-Triggered Bugs
Bug reproduction stands as a pivotal phase in software development, but the absence of configuration information emerges as the main obstacle to effective bug reproduction. Since configuration options generally control critical branches of the software, many bugs can only be triggered under specific configuration settings. We refer to these bugs as configuration-triggered bugs or CTBugs for short. The reproduction of CTBugs consumes considerable time and manual efforts due to the challenges in deducing the missing configuration options within the vast search space of configurations. This complexity contributes to a form of technical debt in software development. To address these challenges, we first conducted an empirical study on 120 CTBugs from 4 widely used systems to understand the root causes and factors influencing the reproduction of CTBugs. Based on our study, we designed and implemented MissConf, the first LLM-enhanced automated tool for CTBug reproduction. MissConf first leverages the LLM to infer whether crucial configuration options are missing in the bug report. Once a suspect CTBug is found, MissConf employs configuration taint analysis and dynamic monitoring methods to filter suspicious configuration options set. Furthermore, it adopts a heuristic strategy for identifying crucial configuration options and their corresponding values. We evaluated MissConf on 5 real-world software systems. The experimental results demonstrate that MissConf successfully infers the 84% (41/49) of the CTBugs and reproduces the 65% (32/49) CTBugs. In the reproduction phase, MissConf eliminates up to 76% of irrelevant configurations, offering significant time savings for developers.
| ICSE24_IndustryChallengeTrack_MissConf_Slides (ICSE24_IndustryChallengeTrack_MissConf_Slides.pdf) | 1.96MiB |
Thu 18 AprDisplayed time zone: Lisbon change
14:00 - 15:30 | LLM, NN and other AI technologies 4Research Track / Industry Challenge Track / New Ideas and Emerging Results at Pequeno Auditório Chair(s): David Nader Palacio William & Mary | ||
14:00 15mTalk | Programming Assistant for Exception Handling with CodeBERT Research Track Yuchen Cai University of Texas at Dallas, Aashish Yadavally University of Texas at Dallas, Abhishek Mishra University of Texas at Dallas, Genesis Montejo University of Texas at Dallas, Tien N. Nguyen University of Texas at Dallas | ||
14:15 15mTalk | An Empirical Study on Noisy Label Learning for Program Understanding Research Track Wenhan Wang Nanyang Technological University, Yanzhou Li Nanyang Technological University, Anran Li Nanyang Technological University, Jian Zhang Nanyang Technological University, Wei Ma Nanyang Technological University, Singapore, Yang Liu Nanyang Technological University Pre-print | ||
14:30 15mTalk | An Empirical Study on Low GPU Utilization of Deep Learning Jobs Research Track Yanjie Gao Microsoft Research, yichen he , Xinze Li Microsoft Research, Bo Zhao Microsoft Research, Haoxiang Lin Microsoft Research, Yoyo Liang Microsoft, Jing Zhong Microsoft, Hongyu Zhang Chongqing University, Jingzhou Wang Microsoft Research, Yonghua Zeng Microsoft, Keli Gui Microsoft, Jie Tong Microsoft, Mao Yang Microsoft Research DOI Pre-print | ||
14:45 15mTalk | Using an LLM to Help With Code Understanding Research Track Daye Nam Carnegie Mellon University, Andrew Macvean Google, Inc., Vincent J. Hellendoorn Carnegie Mellon University, Bogdan Vasilescu Carnegie Mellon University, Brad A. Myers Carnegie Mellon University | ||
15:00 15mTalk | MissConf: LLM-Enhanced Reproduction of Configuration-Triggered Bugs Industry Challenge Track Ying Fu National University of Defense Technology, Teng Wang National University of Defense Technology, Shanshan Li National University of Defense Technology, Jinyan Ding National University of Defense Technolog, Shulin Zhou National University of Defense Technology, Zhouyang Jia National University of Defense Technology, Wang Li National University of Defense Technology, Yu Jiang Tsinghua University, Liao Xiangke National University of Defense Technology File Attached | ||
15:15 7mTalk | XAIport: A Service Framework for the Early Adoption of XAI in AI Model Development New Ideas and Emerging Results Zerui Wang Concordia University, Yan Liu Concordia University, Abishek Arumugam Thiruselvi Concordia University, Wahab Hamou-Lhadj Concordia University, Montreal, Canada DOI Pre-print | ||
15:22 7mTalk | Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code? New Ideas and Emerging Results Alejandro Velasco William & Mary, David Nader Palacio William & Mary, Daniel Rodriguez-Cardenas , Denys Poshyvanyk William & Mary Pre-print | ||