Debug2Fix Framework Integrates Interactive Debugging into AI Coding Agents

A new framework called Debug2Fix has been introduced to enhance the bug-fixing capabilities of AI coding agents by incorporating interactive debugging. Currently, these agents primarily rely on static code analysis or iterative test-fix cycles, which resemble trial-and-error approaches. Developers, however, routinely access rich runtime information during debugging—a resource that coding agents lack due to design limitations. Despite the prevalence of debuggers in modern integrated development environments and command-line tools, they have not been integrated into coding agents. The Debug2Fix framework addresses this gap by making interactive debugging a core component of software engineering agents through a subagent architecture. This approach aims to move beyond manual, developer-driven debugging processes, which remain dominant in software development. The framework was detailed in a research paper with the identifier arXiv:2602.18571v2, which was announced as a replacement cross. The work highlights the significant room for improvement in automating bug fixing, an area where coding agents have made progress but still fall short. By leveraging runtime data, Debug2Fix seeks to provide agents with the same debugging tools that human developers use, potentially leading to more effective and efficient software repair.