STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis
<|reference_start|>STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis: 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.<|reference_end|>
@article{liu2024stall+:,
    title={STALL+: Boosting LLM-based Repository-level Code Completion with Static
  Analysis},
    author={Junwei Liu, Yixuan Chen, Mingwei Liu, Xin Peng, Yiling Lou},
    journal={arXiv preprint arXiv:2406.10018},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10018},
    primaryClass={cs.SE}
}