Enhancing Code Repair with LLMs
Leveraging language models to extract fix ingredients beyond context windows
ScanFix introduces a novel approach that empowers language models to identify and utilize code fix ingredients beyond their standard context limitations.
- Uses an additional scanner model to extract key identifiers from entire codebases
- Demonstrates the crucial importance of identifier ingredients in neural program repair
- Overcomes the inherent context window limitations of language models in repair tasks
- Bridges traditional generate-and-validate approaches with modern neural methods
This research advances software engineering by improving automated bug fixing capabilities, potentially reducing development costs and enhancing code security through more efficient repair processes.