Papers with Repository-level code completion
Late Code Chunking: A Code Chunking Strategy for Repository-Level Code Completion (2026.acl-short)
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| Challenge: | Despite significant advancements in Large Language Models (LLMs), repository-level code completion remains a challenging area. |
| Approach: | They propose a chunking strategy to improve the semantic understanding of code segments for Large Language Models. |
| Outcome: | The proposed strategy improves the semantic understanding of code segments for Large Language Models. |
RepoShapley: Shapley-Enhanced Context Filtering for Repository-Level Code Completion (2026.findings-acl)
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| Challenge: | Large language models have strong reasoning, coding, and generation capabilities, but retrieval-augmented generation remains difficult under fixed context budgets. |
| Approach: | They propose a coalition-aware context filtering framework supervised by Shapley-style marginal contributions that captures sign effects via teacher-forced probing and computes exact Shaply values for small retrieval sets. |
| Outcome: | Experiments show that RepoShapley improves completion quality while reducing harmful context and unnecessary retrieval. |
M2RC-EVAL: Massively Multilingual Repository-level Code Completion Evaluation (2025.acl-long)
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Jiaheng Liu, Ken Deng, Congnan Liu, Jian Yang, Shukai Liu, He Zhu, Peng Zhao, Linzheng Chai, Yanan Wu, JinKe JinKe, Ge Zhang, Zekun Moore Wang, Guoan Zhang, Yingshui Tan, Bangyu Xiang, Zhaoxiang Zhang, Wenbo Su, Bo Zheng
| Challenge: | Existing repository-level code completion benchmarks focus on a limited number of languages . existing benchmarks report overall average scores of different languages ignoring fine-grained abilities . |
| Approach: | They propose to use repository-level code completion benchmarks to evaluate general code intelligence abilities across languages for existing code Large Language Models. |
| Outcome: | The proposed benchmarks improve the code completion abilities of existing LLMs by using two types of annotations on the parsed syntax tree. |
AIRCoder: Adaptive Integration of Multi-dimensional Retrieval for Repository-level Code Completion (2026.acl-long)
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| Challenge: | Existing methods for retrieving code from large codebases use textual similarity or dependency existence, resulting in inconsistent performance. |
| Approach: | They propose a retrieval framework that integrates eight complementary metrics across three dimensions: textual similarity, dependency existence, and structural hierarchy. |
| Outcome: | Experiments on CrossCodeEval and RepoEval show that AIRCoder improves accuracy and performance by 10.2 over baseline methods. |
CodeRAG: Finding Relevant and Necessary Knowledge for Retrieval-Augmented Repository-Level Code Completion (2025.emnlp-main)
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| Challenge: | Recent advances in code large language models have produced repository-level code completion methods that automatically predict the unfinished code based on the broader information from the repository. |
| Approach: | They propose a framework to identify relevant knowledge for retrieval-augmented repository-level code completion. |
| Outcome: | The proposed framework significantly outperforms state-of-the-art methods on ReccEval and CCEval. |