OpenAI的Codex能修复bug吗?:对QuixBugs的评价

Julian Aron Prenner, Hlib Babii, R. Robbes
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引用次数: 51

摘要

OpenAI的Codex是一个在大型代码语料库上训练的类似gpt -3的模型,已经成为学术界内外的头条新闻。给定用户提供的简短描述,它能够合成在大多数情况下在语法和语义上都有效的代码片段。在这项工作中,我们想调查Codex是否能够定位和修复错误,这是自动程序修复中的两项重要任务。我们最初的评估使用了多语言的QuixBugs基准测试(Python和Java中都有40个bug)。我们发现,尽管没有接受过APR培训,但食品法典的有效性令人惊讶,与最新的技术相比具有竞争力。我们的结果还表明,Codex在修复Python方面比Java更成功,修复的Python错误多50%。
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Can OpenAI's Codex Fix Bugs?: An evaluation on QuixBugs
OpenAI's Codex, a GPT-3like model trained on a large code corpus, has made headlines in and outside of academia. Given a short user-provided description, it is capable of synthesizing code snippets that are syntactically and semantically valid in most cases. In this work, we want to investigate whether Codex is able to localize and fix bugs, two important tasks in automated program repair. Our initial evaluation uses the multi-language QuixBugs benchmark (40 bugs in both Python and Java). We find that, despite not being trained for APR, Codex is surprisingly effective, and competitive with recent state of the art techniques. Our results also show that Codex is more successful at repairing Python than Java, fixing 50% more bugs in Python.
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