Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion

• URL: http://arxiv.org/abs/2405.19782v1
• Date: Thu, 30 May 2024 07:48:00 GMT
• Title: Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion
• Authors: Wei Cheng, Yuhan Wu, Wei Hu,
• Abstract summary: We propose a dataflow-guided retrieval augmentation approach, called DraCo, for repository-level code completion. 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.
• Score: 17.4397495929138
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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