Fixing bugs and problems in code repositories can be difficult in software engineering. Imagine you encounter a bug in a GitHub repository and you don’t know how to fix it! While some solutions are available to help with this problem, they may not always be effective or efficient.

An existing solution is to manually search for and fix code repository errors. This process involves developers spending hours reading code, finding problems, and making fixes. While this approach can yield results, it is time-consuming and can lead to human error.

Come across SWE-agent, a software engineering agent that turns language models (such as 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), simplifies the process of interacting with code repositories, making it easier for language models to understand and troubleshoot issues.

A key feature of SWE-agent is its linter, which checks code syntax before allowing changes to be made. This helps prevent errors and ensures that any changes the agent makes are syntactically correct. In addition, SWE-agent includes a specialized file viewer and directory search tool, making it easier for language models to navigate and understand code repositories.

The effectiveness of SWE-agent is proven by its impressive measurements. In the full SWE-bench test set, SWE-agent solves 12.29% of the problems, achieving peak performance. This shows the power of using language models as software engineering agents and the importance of a well-designed interface, such as ACI, to maximize their potential.

In summary, SWE-agent is an innovative solution for fixing bugs and problems in code repositories. By leveraging language models and a carefully designed interface, it simplifies software engineering, making it faster, more efficient, and less error-prone.