AutoCodeRover

AutoCodeRover is an AI-powered software that automates the process of resolving GitHub issues, such as bug fixing and feature addition. It combines language models with analysis and debugging capabilities to prioritize patch locations and generate effective patches. With a success rate of 16% for SWE-bench issues and 22% for SWE-bench lite issues, AutoCodeRover outperforms other AI software engineers. It has two unique features: Program Structure Aware code search APIs and the ability to leverage test cases for higher repair rates.

Key Features:

🔎 Context retrieval: AutoCodeRover uses code search APIs to navigate the codebase and collect relevant context for issue resolution.

💊 Patch generation: The software attempts to write patches based on the retrieved context, effectively resolving GitHub issues.

Use Cases:

1. Bug fixing: AutoCodeRover can automatically fix bugs in software projects by identifying the appropriate patch locations and generating effective patches.

2. Feature addition: The software can also help in adding new features to software projects by analyzing the codebase and providing relevant context for patch generation.

3. Test case enhancement: With access to test cases, AutoCodeRover can further improve issue resolution by performing statistical fault localization, leading to more accurate and efficient patch generation.

Conclusion:

AutoCodeRover is an innovative AI product that streamlines the process of resolving GitHub issues by automating bug fixing, feature addition, and test case enhancement. With its unique features and impressive success rates, it offers a practical and efficient solution for software engineers. Experience the power of AutoCodeRover today and witness how it can transform your software development process.