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Debug-gym Can AI agents lighten developers debugging load

2025-04-10•Brenda Potts•Original Link•2 minutes
AI
Debugging
SoftwareDevelopment

Developers spend a lot of time debugging code. Learn how debug-gym can equip AI agents to help, enabling them to set breakpoints, navigate the codebase, and print runtime variable values on demand, so they better understand the code and its execution flow.

A graphic with a gradient background transitioning from blue on the left to pink on the right. The graphic features a white outline of a computer monitor with code brackets on the screen, an arrow pointing downwards into the monitor, and another arrow curving around to point upwards towards a magnifying glass with a bug icon inside it.

The Growing Role of AI in Coding

With the rise of AI coding tools like GitHub Copilot, developers are increasingly relying on AI to generate code. GitHub CEO Thomas Dohmke predicted that "sooner than later, 80% of the code is going to be written by Copilot." This trend is evident in startups, where 95% of code for a quarter of Y Combinator’s latest batch was written by large language models (LLMs).

However, most developers spend the majority of their time debugging code, not writing it. This raises an important question: Can AI tools also assist in debugging?

Introducing Debug-gym

Microsoft Research has developed debug-gym, an environment that equips AI agents with interactive debugging tools. Unlike traditional AI coding tools, debug-gym allows agents to:

  • Set breakpoints
  • Navigate codebases
  • Print runtime variable values
  • Create test functions

Figure 1: Diagram demonstrating the code-repairing process in outline. Left: conventional code-repairing system; right: additional tools enabled by debug-gym.

Key Features of Debug-gym

  • Repository-level information: Agents can navigate and edit files across the entire repository.
  • Robust and safe: Code runs in sandboxed Docker containers to prevent harmful actions.
  • Extensible: Easily add new tools to the environment.
  • Text-based: Compatible with modern LLM-based agents.

Early Results and Future Work

Initial experiments show promising results. While current AI tools struggle with complex debugging tasks, debug-gym-enabled agents show significant improvement. For example, on the SWE-bench Lite benchmark, agents with debugging tools performed better than those without.

Figure 2: The success rate represents the percentage of the 300 SWE-bench Lite issues resolved, comparing between agents with and without debugging tools.

Next Steps

Microsoft plans to:

  1. Fine-tune LLMs for interactive debugging.
  2. Develop specialized data for training debugging agents.
  3. Open-source debug-gym to encourage community collaboration.

Conclusion

Debug-gym represents a significant step forward in AI-assisted debugging. By enabling AI agents to interactively seek information and propose fixes, it has the potential to drastically reduce developers' debugging workload. For more details, check out the technical report and GitHub repository.

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