Title: MULocBench: Beyond Code Benchmark for Multi-Type Software Issue Localization
Primary area: datasets and benchmarks
Keywords: Issue Localization; LLM; SWE-Bench;

Abstract: Accurate project localization (e.g., files and functions) for issue resolution is a critical first step in software maintenance. 
However, existing benchmarks for issue localization, such as SWE-Bench and LocBench, are limited. 
They focus predominantly on pull-request issues and code locations, ignoring other evidence and non-code files such as commits, comments, configurations, and documentation. 
To address this gap, we introduce MULocBench, a comprehensive dataset of 1,100 issues from 46 popular GitHub Python projects. 
Comparing with existing benchmarks, MULocBench offers greater diversity in issue types, root causes, location scopes, and file types, providing a more realistic testbed for evaluation. 
Using this benchmark, we assess the performance of state-of-the-art localization methods and five LLM-based prompting strategies. Our results reveal significant limitations in current techniques: even at the file level, performance metrics (Acc@5, F1) remain below 40%. This underscores the challenge of generalizing to realistic, multi-faceted issue resolution. To enable future research on project localization for issue resolution, we publicly release MULocBench at https://huggingface.co/datasets/somethingone/MULocBench.