Fixing bugs and issues in code repositories can be challenging in software engineering. Imagine encountering a bug in a GitHub repository and not knowing how to fix it! While some solutions are available to help with this problem, they may not always be efficient or effective.
One existing solution is manually searching for and fixing code repository bugs. This process involves developers spending hours reading through code, identifying issues, and making corrections. Although this approach can yield results, it consumes time and may result in human errors.
Meet SWE-agent, a software engineering agent that turns language models (like GPT-4) into powerful tools for fixing bugs and issues in real GitHub repositories. SWE-agent provides a simple interface for language models to browse repositories, view, edit, and execute code files. This interface, called the Agent-Computer Interface (ACI), streamlines the process of interacting with code repositories, making it easier for language models to understand and address issues.
One key feature of SWE-agent is its linter, which checks code syntax before allowing edits to be made. This helps prevent errors and ensures that any changes the agent makes are syntactically correct. Additionally, SWE-agent includes a specialized file viewer and directory searching tool, making it easier for language models to navigate and understand code repositories.
The effectiveness of SWE-agent is demonstrated by its impressive metrics. On the full SWE-bench test set, SWE-agent resolves 12.29% of issues, achieving state-of-the-art performance. This shows the power of using language models as software engineering agents and the importance of a well-designed interface, like the ACI, for maximizing their capabilities.
In summary, SWE-agent is an innovative solution for fixing bugs and issues in code repositories. By leveraging language models and a carefully designed interface, it streamlines software engineering, making it faster, more efficient, and less error-prone.
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