SaraCoder: Orchestrating Semantic and Structural Cues for Resource-Optimized Repository-Level Code Completion

• URL: http://arxiv.org/abs/2508.10068v2
• Date: Mon, 13 Oct 2025 07:16:49 GMT
• Title: SaraCoder: Orchestrating Semantic and Structural Cues for Resource-Optimized Repository-Level Code Completion
• Authors: Xiaohan Chen, Zhongying Pan, Quan Feng, Yu Tian, Shuqun Yang, Mengru Wang, Lina Gong, Yuxia Geng, Piji Li, Xiang Chen,
• Abstract summary: We propose a resource-optimized retrieval augmentation method, SaraCoder.<n>It maximizes information diversity and representativeness in a limited context window.<n>Our work proves that systematically refining retrieval results across multiple dimensions provides a new paradigm for building more accurate and resource-optimized repository-level code completion systems.
• Score: 34.41683042851225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite Retrieval-Augmented Generation improving code completion, traditional retrieval methods struggle with information redundancy and a lack of diversity within limited context windows. To solve this, we propose a resource-optimized retrieval augmentation method, SaraCoder. It maximizes information diversity and representativeness in a limited context window, significantly boosting the accuracy and reliability of repository-level code completion. Its core Hierarchical Feature Optimization module systematically refines candidates by distilling deep semantic relationships, pruning exact duplicates, assessing structural similarity with a novel graph-based metric that weighs edits by their topological importance, and reranking results to maximize both relevance and diversity. Furthermore, an External-Aware Identifier Disambiguator module accurately resolves cross-file symbol ambiguity via dependency analysis. Extensive experiments on the challenging CrossCodeEval and RepoEval-Updated benchmarks demonstrate that SaraCoder outperforms existing baselines across multiple programming languages and models. Our work proves that systematically refining retrieval results across multiple dimensions provides a new paradigm for building more accurate and resource-optimized repository-level code completion systems.

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