ContextModule: Improving Code Completion via Repository-level Contextual Information
Zhanming Guan, Junlin Liu, Jierui Liu, Chao Peng, Dexin Liu, Ningyuan, Sun, Bo Jiang, Wenchao Li, Jie Liu, Hang Zhu

TL;DR
This paper introduces ContextModule, a framework that enhances code completion by integrating repository-level context, user behavior, and static analysis to improve accuracy and relevance in real-time environments.
Contribution
The paper presents a novel framework that incorporates repository-wide contextual information into LLM-based code completion, addressing limitations of file-only context.
Findings
Significant improvement in code completion accuracy.
Enhanced relevance of suggestions based on repository context.
Maintained low latency with performance optimizations.
Abstract
Large Language Models (LLMs) have demonstrated impressive capabilities in code completion tasks, where they assist developers by predicting and generating new code in real-time. However, existing LLM-based code completion systems primarily rely on the immediate context of the file being edited, often missing valuable repository-level information, user behaviour and edit history that could improve suggestion accuracy. Additionally, challenges such as efficiently retrieving relevant code snippets from large repositories, incorporating user behavior, and balancing accuracy with low-latency requirements in production environments remain unresolved. In this paper, we propose ContextModule, a framework designed to enhance LLM-based code completion by retrieving and integrating three types of contextual information from the repository: user behavior-based code, similar code snippets, and…
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Taxonomy
TopicsSemantic Web and Ontologies · Service-Oriented Architecture and Web Services · Natural Language Processing Techniques
