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 remains an open question. We find that existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of LLMs.%0A To address the knowledge gap%2C we propose a new benchmark named DevEval%2C which has three advances. (1) DevEval aligns with real-world repositories in multiple dimensions%2C e.g.%2C code distributions and dependency distributions. (2) DevEval is annotated by 13 developers and contains comprehensive annotations (e.g.%2C requirements%2C original repositories%2C reference code%2C and reference dependencies). (3) DevEval comprises 1%2C874 testing samples from 117 repositories%2C covering 10 popular domains (e.g.%2C Internet%2C Database). Based on DevEval%2C we propose repository-level code generation and evaluate 8 popular LLMs on DevEval (e.g.%2C gpt-4%2C gpt-3.5%2C StarCoder 2%2C DeepSeek Coder%2C CodeLLaMa). Our experiments reveal these LLMs' coding abilities in real-world code repositories. For example%2C in our experiments%2C the highest Pass%401 of gpt-4-turbo is only 53.04%25. We also analyze LLMs' failed cases and summarize their shortcomings. We hope DevEval can facilitate the development of LLMs in real code repositories. DevEval%2C prompts%2C and LLMs' predictions have been released.?quality=80&w=800)
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