Enhancing Project-Specific Code Completion by Inferring Internal API Information

作者：Le Deng, Xiaoxue Ren, Chao Ni, Ming Liang, David Lo, Zhongxin Liu · 发表于：IEEE Transactions on Software Engineering · 年份：2025 · DOI：10.1109/tse.2025.3592823 · 被引用次数：5 · 研究领域：Software Engineering Research、Software System Performance and Reliability、Service-Oriented Architecture and Web Services

Project-specific code completion, which aims to complete code based on the context of the project, is an important and practical software engineering task. The state-of-the-art approaches employ the retrieval-augmented generation (RAG) paradigm and prompt large language models (LLMs) with information retrieved from the target project for project-specific code completion. In practice, developers always define and use custom functionalities, namely internal APIs, to facilitate the implementation of specific project requirements. Thus, it is essential to consider internal API information for accurate project-specific code completion. However, existing approaches either retrieve similar code snippets, which do not necessarily contain related internal API information, or retrieve internal API information based on import statements, which usually do not exist when the related internal APIs haven’t been used in the file. Therefore, these project-specific code completion approaches face challenges in effectiveness or practicability. To this end, this paper aims to enhance project-specific code completion by locating internal API information without relying on import statements. We first propose a method to infer internal API information. Our method first extends the representation of each internal API by constructing its usage examples and functional semantic information (i.e., a natural language description of the function’s purpose) and constructs a knowledge base. Based on the kno...