arXiv:2601.19697v2 Announce Type: replace-cross Abstract: Repository-level code completion remains a challenging task for existing code large language models (code LLMs) due to their limited understanding of repository-specific context and domain knowledge. While retrieval-augmented generation (RAG) approaches have shown promise by retrieving relevant code snippets as cross-file context, they suffer from two fundamental problems: misalignment between the query and the target code in the retrieval process, and the inability of existing retrieval methods to effectively utilize the inference info
The ongoing rapid development of large language models and their application to code generation necessitates continuous improvements in accuracy and contextual understanding, specifically for complex repository-level tasks.
Improved code completion models that leverage repository-specific context can significantly enhance developer productivity, reduce errors, and accelerate software development cycles at scale.
This advancement proposes a new method to better align retrieval with programming intent in RAG-based code LLMs, potentially overcoming current limitations in understanding complex, cross-file codebases.
- · Software developers
- · Companies investing in AI-powered developer tools
- · Large language model providers
- · Open-source software communities
More accurate and context-aware code completion within integrated development environments.
Accelerated development of complex software projects, reducing time-to-market for new applications.
A potential shift in programmer roles towards higher-level design and architectural work, with AI handling more boilerplate code.
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Read at arXiv cs.AI