Uncertainty-Aware Framework for Code Generation
A recent research paper presents OpenCoder, a framework designed for repository-level code generation that takes uncertainty into account. This study tackles the challenges posed by heterogeneous evidence, including similar code samples, repository context, and project-specific APIs, which can often be noisy, redundant, or contradictory. Unlike current retrieval-augmented techniques that prioritize relevance, OpenCoder assesses source-specific uncertainty, organizes and ranks evidence, and directs the processes of generation, verification, and repair. A factorial analysis indicates that there is no single additive ranking for sources; instead, interactions between sources vary based on the evidence provided and the LLM backend. The framework underwent testing on an enhanced 32-task RepoE benchmark.
Key facts
- OpenCoder is an uncertainty-aware framework for code generation.
- It estimates source-specific uncertainty from heterogeneous evidence.
- Evidence types include similar-code examples, repository context, and APIs.
- Existing methods optimize retrieval relevance but ignore uncertainty.
- Factorial analysis shows no universal additive source ranking.
- Cross-source interactions depend on evidence and LLM backend.
- Tested on an expanded 32-task RepoE benchmark.
- The paper is published on arXiv with ID 2607.24884.
Entities
Institutions
- arXiv