STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis

• URL: http://arxiv.org/abs/2406.10018v1
• Date: Fri, 14 Jun 2024 13:28:31 GMT
• Title: STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis
• Authors: Junwei Liu, Yixuan Chen, Mingwei Liu, Xin Peng, Yiling Lou,
• Abstract summary: This work performs the first study on the static analysis integration in LLM-based repository-level code completion. We first implement a framework STALL+, which supports an extendable and customizable integration of multiple static analysis strategies. Our findings show that integrating file-level dependencies in prompting phase performs the best while the integration in post-processing phase performs the worse.
• Score: 8.059606338318538
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Repository-level code completion is challenging as it involves complicated contexts from multiple files in the repository. To date, researchers have proposed two technical categories to enhance LLM-based repository-level code completion, i.e., retrieval-augmented generation (RAG) and static analysis integration. This work performs the first study on the static analysis integration in LLM-based repository-level code completion by investigating both the effectiveness and efficiency of static analysis integration strategies across different phases of code completion. We first implement a framework STALL+, which supports an extendable and customizable integration of multiple static analysis strategies into the complete pipeline of LLM-based repository-level code completion; and based on STALL+, we perform extensive experiments by including different code LLMs on the latest repository-level code completion benchmark CrossCodeEval. Our findings show that integrating file-level dependencies in prompting phase performs the best while the integration in post-processing phase performs the worse. Additionally, we observe different improvements from static analysis between dynamic languages and static languages, i.e., the best combination is prompting-phase with decoding-phase integration for Java while the best combination is prompting-phase with post-processing-phase integration for Python given the limitations of statically analyzing dynamic languages. Additionally, we find the complementarity between RAG and static analysis integration as well as their cost-effectiveness after combination.

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