Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving
Authors: name: Daoguang Zan, name: Zhirong Huang, name: Wei Liu, name: Hanwu Chen, name: Linhao Zhang, name: Shulin Xin, name: Lu Chen, name: Qi Liu, name: Xiaojian Zhong, name: Aoyan Li, name: Siyao Liu, name: Yongsheng Xiao, name: Liangqiang Chen, name: Yuyu Zhang, name: Jing Su, name: Tianyu Liu, name: Rui Long, name: Kai Shen, name: Liang Xiang
Organization: ByteDance Seed

The task of issue resolving is to modify a codebase to generate a patch that
addresses a given issue. However, existing benchmarks, such as SWE-bench, focus
almost exclusively on Python, making them insufficient for evaluating Large
Language Models (LLMs) across diverse software ecosystems. To address this, we
introduce a multilingual issue-resolving benchmark, called Multi-SWE-bench,
covering Java, TypeScript, JavaScript, Go, Rust, C, and C++. It includes a
total of 1,632 high-quality instances, which were carefully annotated from
2,456 candidates by 68 expert annotators, ensuring that the benchmark can
provide an accurate and reliable evaluation. Based on Multi-SWE-bench, we
evaluate a series of state-of-the-art models using three representative methods
(Agentless, SWE-agent, and OpenHands) and present a comprehensive analysis with
key empirical insights. In addition, we launch a Multi-SWE-RL open-source
community, aimed at building large-scale reinforcement learning (RL) training
datasets for issue-resolving tasks. As an initial contribution, we release a
set of 4,723 well-structured instances spanning seven programming languages,
laying a solid foundation for RL research in this domain. More importantly, we
open-source our entire data production pipeline, along with detailed tutorials,
encouraging the open-source community to continuously contribute and expand the
dataset. We envision our Multi-SWE-bench and the ever-growing Multi-SWE-RL
community as catalysts for advancing RL toward its full potential, bringing us
one step closer to the dawn of AGI.