The task of issue resolving aims to modify a codebase to generate a patch that addresses a given issue. However, most existing benchmarks focus almost exclusively on Python, making them insufficient for evaluating Large Language Models (LLMs) across different programming languages. To bridge this gap, we introduce a multilingual issue-resolving benchmark, called Multi-SWE-bench, covering 8 languages of Python, Java, TypeScript, JavaScript, Go, Rust, C, and C++. In particular, this benchmark includes a total of 2,132 high-quality instances, carefully curated by 68 expert annotators, ensuring a reliable and accurate evaluation of LLMs on the issue-resolving task. Based on human-annotated results, the issues are further classified into three difficulty levels. We evaluate a series of state-of-the-art models on Multi-SWE-bench, utilizing both procedural and agent-based frameworks for issue resolving. Our experiments reveal three key findings: (1) Limited generalization across languages: While existing LLMs perform well on Python issues, their ability to generalize across other languages remains limited; (2) Performance aligned with human-annotated difficulty: LLM-based agents' performance closely aligns with human-assigned difficulty, with resolution rates decreasing as issue complexity rises; and (3) Performance drop on cross-file issues: The performance of current methods significantly deteriorates when handling cross-file issues. These findings highlight the limitations of current LLMs and underscore the need for more robust models capable of handling a broader range of programming languages and complex issue scenarios.

Improving boundary segmentation results has recently attracted increasing attention in the field of semantic segmentation. Since existing popular methods usually exploit the long-range context, the boundary cues are obscure in the feature space, leading to poor boundary results. In this paper, we propose a novel conditional boundary loss (CBL) for semantic segmentation to improve the performance of the boundaries. The CBL creates a unique optimization goal for each boundary pixel, conditioned on its surrounding neighbors. The conditional optimization of the CBL is easy yet effective. In contrast, most previous boundary-aware methods have difficult optimization goals or may cause potential conflicts with the semantic segmentation task. Specifically, the CBL enhances the intra-class consistency and inter-class difference, by pulling each boundary pixel closer to its unique local class center and pushing it away from its different-class neighbors. Moreover, the CBL filters out noisy and incorrect information to obtain precise boundaries, since only surrounding neighbors that are correctly classified participate in the loss calculation. Our loss is a plug-and-play solution that can be used to improve the boundary segmentation performance of any semantic segmentation network. We conduct extensive experiments on ADE20K, Cityscapes, and Pascal Context, and the results show that applying the CBL to various popular segmentation networks can significantly improve the mIoU and boundary F-score performance.