ContextModule: Improving Code Completion via Repository-level Contextual Information

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 critical symbol definitions. By capturing user interactions across files and leveraging repository-wide static analysis, ContextModule improves the relevance and precision of generated code. We implement performance optimizations, such as index caching, to ensure the system meets the latency constraints of real-world coding environments. Experimental results and industrial practise demonstrate that ContextModule significantly improves code completion accuracy and user acceptance rates.