Toward Executable Repository-Level Code Generation via Environment Alignment

1. 1Train large language models on a dataset of repository-level code
2. 2Align the model's environment with the target repository's dependencies and internal references
3. 3Use the model to generate a multi-file repository that can be installed and launched
4. 4Validate the generated repository through executable validation

This research benefits software engineers and AI researchers working on code generation and automation, as it enables the creation of executable code repositories

💡 Environment alignment is crucial for generating executable code repositories

💡 Executable repository-level code generation via environment alignment with LLMs