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. MISS-CONF 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 re-sults demonstrate that MISSCONF successfully infers the 84% (41/49) of the CTBugs and reproduces the 65% (32/49) CTBugs. In the re-production phase, MISSCONF eliminates up to 76% of irrelevant configurations, offering significant time savings for developers.