AutoCodeRover's context retrieval can be enhanced by incorporating more advanced program analysis techniques such as static code analysis and dynamic program slicing. By analyzing the codebase at a deeper level, AutoCodeRover can extract more precise and relevant code snippets for issue resolution. Additionally, integrating machine learning models specifically trained for code understanding can improve the accuracy of context retrieval.
In terms of patch generation, AutoCodeRover can benefit from a more diverse set of patch generation strategies. By incorporating techniques like program synthesis and code refactoring patterns, AutoCodeRover can generate more sophisticated and effective patches. Furthermore, leveraging historical code repositories and version control systems can provide valuable insights for generating patches that align with the project's coding conventions and standards.