Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion
<|reference_start|>Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion: Recent years have witnessed the deployment of code language models (LMs) in various code intelligence tasks such as code completion. Yet, it is challenging for pre-trained LMs to generate correct completions in private repositories. Previous studies retrieve cross-file context based on import relations or text similarity, which is insufficiently relevant to completion targets. In this paper, we propose a dataflow-guided retrieval augmentation approach, called DraCo, for repository-level code completion. DraCo parses a private repository into code entities and establishes their relations through an extended dataflow analysis, forming a repo-specific context graph. Whenever triggering code completion, DraCo precisely retrieves relevant background knowledge from the repo-specific context graph and generates well-formed prompts to query code LMs. Furthermore, we construct a large Python dataset, ReccEval, with more diverse completion targets. Our experiments demonstrate the superior accuracy and applicable efficiency of DraCo, improving code exact match by 3.43% and identifier F1-score by 3.27% on average compared to the state-of-the-art approach.<|reference_end|>
@article{cheng2024dataflow-guided,
    title={Dataflow-Guided Retrieval Augmentation for Repository-Level Code
  Completion},
    author={Wei Cheng and Yuhan Wu and Wei Hu},
    journal={arXiv preprint arXiv:2405.19782},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19782},
    primaryClass={cs.SE cs.CL}
}