The widespread use of Large Language Models (LLMs) in software engineering has intensified the need for improved model and resource efficiency. In particular, for neural code generation, LLMs are used to translate function/method signature and DocString to executable code. DocStrings which capture user re quirements for the code and used as the prompt for LLMs, often contains redundant information. Recent advancements in prompt compression have shown promising results in Natural Language Processing (NLP), but their applicability to code generation remains uncertain. Our empirical study show that the state-of-the-art prompt compression methods achieve only about 10% reduction, as further reductions would cause significant performance degradation. In our study, we propose a novel compression method, ShortenDoc, dedicated to DocString compression for code generation. Our extensive experiments on six code generation datasets, five open-source LLMs (1B to 10B parameters), and one closed-source LLM GPT-4o confirm that ShortenDoc achieves 25-40% compression while preserving the quality of generated code, outperforming other baseline methods at similar compression levels. The benefit of this research is to improve efficiency and reduce the cost while maintaining the quality of the generated code, especially when calling third-party APIs, and is able to reduce the token processing cost by 25-40%.

翻译：大型语言模型（LLM）在软件工程中的广泛应用加剧了对提升模型与资源效率的需求。特别是在神经代码生成任务中，LLM被用于将函数/方法签名及文档字符串（DocString）转换为可执行代码。文档字符串作为用户对代码需求的描述，并作为LLM的输入提示，通常包含冗余信息。近期提示压缩技术在自然语言处理（NLP）领域已展现出良好效果，但其在代码生成中的适用性仍不明确。我们的实证研究表明，当前最先进的提示压缩方法仅能实现约10%的压缩率，进一步压缩将导致生成性能显著下降。本研究提出一种专为代码生成设计的文档字符串压缩新方法ShortenDoc。我们在六个代码生成数据集、五个开源LLM（参数量1B至10B）及一个闭源模型GPT-4o上开展的广泛实验证实，ShortenDoc能在保持生成代码质量的同时实现25-40%的压缩率，在同等压缩水平下优于其他基线方法。本研究的价值在于：在维持生成代码质量的前提下提升效率并降低成本，尤其在调用第三方API时，能够减少25-40%的令牌处理开销。