标题： 扭转局势：基于仓库的代码反射 显示英文标题

标题： Turning the Tide: Repository-based Code Reflection

摘要： 代码大型语言模型（LLMs）通过理解并跨语言生成代码来增强编程，通过反思提供智能反馈、错误检测和代码更新，提高开发效率和可访问性。 尽管基准测试（例如 HumanEval/LiveCodeBench）评估代码生成和现实相关性，但之前的工作忽略了在代码库中修改代码的场景。 考虑到在提升反思能力以及在动态基准中避免数据污染方面的挑战，我们引入了LiveRepoReflection，这是一个具有挑战性的基准测试，用于评估多文件代码库环境中的代码理解和生成，包含跨越$6$种编程语言的1,888个经过严格筛选的测试用例，以确保多样性、正确性和高难度。 此外，我们创建了 RepoReflection-Instruct，一个大规模、质量筛选过的指令微调数据集，来源于多种来源，用于通过涉及代码生成和错误驱动修复的两轮对话过程训练RepoReflectionCoder。 排行榜评估了超过40个LLMs，以反映基于代码库的代码反思模型性能。 显示英文摘要

摘要： Code large language models (LLMs) enhance programming by understanding and generating code across languages, offering intelligent feedback, bug detection, and code updates through reflection, improving development efficiency and accessibility. While benchmarks (e.g. HumanEval/LiveCodeBench) evaluate code generation and real-world relevance, previous works ignore the scenario of modifying code in repositories. Considering challenges remaining in improving reflection capabilities and avoiding data contamination in dynamic benchmarks, we introduce LiveRepoReflection, a challenging benchmark for evaluating code understanding and generation in multi-file repository contexts, featuring 1,888 rigorously filtered test cases across $6$ programming languages to ensure diversity, correctness, and high difficulty. Further, we create RepoReflection-Instruct, a large-scale, quality-filtered instruction-tuning dataset derived from diverse sources, used to train RepoReflectionCoder through a two-turn dialogue process involving code generation and error-driven repair. The leaderboard evaluates over 40 LLMs to reflect the model performance of repository-based code reflection.