Recently, the large language models (LLMs) have shown extraordinary ability in understanding natural language and generating programming code. It has been a common practice of software engineers to consult LLMs when encountering coding questions. Although efforts have been made to avoid syntax errors and align the code with the intended semantics, the reliability and robustness of the code generationfrom LLMs have not yet been thoroughly studied. The executable code is not equivalent to the reliable and robust code, especially in the context of real-world software development. The misuse of APIs in the generated code could lead to severe problem, such as resource leaks, program crashes. To make things worse, the users of LLM code generation services are actually the developers that are most vulnerable to these code that seems right -- They are always novice developers that are not familiar with the APIs that LLMs generate code for them. Therefore, they could hardly tell the misuse in the code generated by LLMs, which further facilitates the incorrect code applied in real-world software. Existing code evaluation benchmark and datasets focus on crafting small tasks such as programming questions in coding interviews, which however deviates from the problem that developers would ask LLM for real-world coding help. To fill the missing piece, in this work, we propose a dataset RobustAPI for evaluating the reliability and robustness of code generated by LLMs. We collect 1208 coding questions from StackOverflow on 24 representative Java APIs. We summarize thecommon misuse patterns of these APIs and evaluate them oncurrent popular LLMs. The evaluation results show that evenfor GPT-4, 62% of the generated code contains API misuses,which would cause unexpected consequences if the code isintroduced into real-world software.

翻译：近年来，大型语言模型（LLMs）在理解自然语言和生成编程代码方面展现出卓越能力。软件工程师在遇到编码问题时咨询LLMs已成为常见做法。尽管已有研究致力于避免语法错误并使代码与预期语义对齐，但LLMs代码生成的可靠性和鲁棒性尚未得到充分研究。可执行代码并不等同于可靠且鲁棒的代码，尤其是在实际软件开发场景中。生成代码中API的误用可能引发严重问题，如资源泄漏、程序崩溃。更棘手的是，LLM代码生成服务的用户恰恰是最容易受到这些看似正确代码影响的开发者——他们往往是刚接触LLM生成代码所涉及API的初级开发者。此类群体难以辨别LLM生成代码中的API误用，从而进一步助长错误代码被应用于实际软件。现有代码评估基准与数据集主要聚焦于编程面试等小型任务设计，这与开发者向LLM寻求真实编码帮助的实际需求存在偏差。为填补这一空白，本文提出RobustAPI数据集，用于评估LLM生成代码的可靠性与鲁棒性。我们从StackOverflow收集了覆盖24个代表性Java API的1208个编码问题，总结了这些API的常见误用模式，并基于当前流行LLMs进行评估。评估结果表明，即使对于GPT-4，仍有62%的生成代码存在API误用，若将这些代码引入实际软件将导致不可预料的后果。