AI coding assistants like GitHub Copilot or ChatGPT are fantastic at whipping up chunks of Python or JavaScript. But when it comes to spotting bugs or fixing broken codebases? Let’s just say… they’re still learning. Debugging is where most human developers burn hours, sipping coffee and squinting at stack traces, and it turns out AI isn’t quite ready to take over that part of the job.
That’s exactly what Microsoft Research is trying to change with a fresh new tool called Debug-gym. This thing will help in training AI coders to get better at fixing code, not just writing it. So, let’s dive into what Debug-gym is.
Table of Contents
What Is Debug-gym?
Picture this: You’re giving your AI assistant a puzzle to solve, but instead of handing it all the clues up front, you’re letting it poke around, ask questions, use tools, and figure things out as a real developer would.
That’s Debug-gym in a nutshell. It’s a text-based interactive environment where large language models (LLMs) can explore actual codebases, set breakpoints, run tests, and apply fixes. It’s like handing them a keyboard and a debugger instead of just hoping they guess the right answer from past experience.
It’s built specifically to train and test AI models on realistic debugging tasks, using actual tools like Python’s pdb, file viewers, and test runners.
How Debug-gym Works
At its core, Debug-gym is made up of three main parts:
1. The Agent
This is the AI model that runs the show, like GPT -4 or Claude Sonnet. It gets a description of the bug, reads the code, and figures out what to do next.
2. The Toolbox
The agent can use tools like:
- view – This lets the AI open up a specific file and read its contents. Like scrolling through code in VS Code.
- eval – It executes the code. If there are test cases, it checks if the bug is fixed. If not, it returns the error message.
- pdb – It’s the Python Debugger. It allows the AI to step through code line by line, set and clear breakpoints and inspect values of variables at runtime. It’s the same tool real developers use when print statements aren’t cutting it.
- rewrite – It edits the code. The AI specifies the file, line numbers, and the new content.
- listdir – It helps the AI explore big repos without dumping everything at once. It can look into subfolders, just like a dev would do with a file explorer.
These tools let the AI explore the project like a real engineer would.
3. The Environment
This is the full codebase, often dropped inside a Docker container for safety. The AI can read files, try running the app, or even test its fixes, all within a sandbox that protects the real world from any chaos.
A Real-Life Example of Debug-gym Working
Let’s say an AI agent is trying to fix a Python script that calculates the median house price from a dataset. The script crashes on a line that says:
median = df[‘Price’].median()
But the column’s name is actually ‘prix’, which is a sneaky typo that’s tough to spot. A traditional AI might see the error (KeyError: ‘Price’), try changing ‘Price’ to something random, and repeat until it gets lucky or gives up.
But the AI model using Debug-gym sets a breakpoint at the error line, prints the list of column names, sees ‘prix’, not ‘Price’, rewrites the line using the correct name and runs the test, passes, and its debugging is successfully done.
It’s not just guessing—it’s investigating.
AI Agents of Debug-gym
Debug-gym ships with three demo agents to show how things work. Think of them as beginner, intermediate, and smart-but-cautious.
1. Rewrite Agent
This guy can read code, fix it, and test it—but no fancy tools. Just basic editing and testing.
2. Debug Agent
This one can use pdb. So it can really investigate the code before making changes. It’s slower but smarter.
3. Debug(5) Agent
A clever hybrid. It starts off just editing, like the rewrite agent. But after five failed rewrites, it unlocks pdb, kinda like a power-up. Turns out this combo often works best.
Performance Evaluation of AI Models Using Debug-Gym
Microsoft tested several AI models using debug-gym, from smaller open-source models to large commercial ones like GPT-4o and Claude 3.7 Sonnet.
The results were revealing. Even powerful models struggle with interactive debugging tasks. Models with stronger reasoning capabilities performed better. Adding debugging tools improved the performance of these models significantly, especially for complex bugs. Models like Claude 3.7 Sonnet that explored code more thoroughly tended to perform better.
On the SWE-bench benchmark (real-world GitHub issues), the best-performing model only solved about 52% of problems, even with debugging tools. Without them, performance dropped to 37%.
This gap shows that while AI coding has improved dramatically, there’s still a significant distance between AI and expert human developers when it comes to debugging skills.
How to Get Started With Debug-gym
For the developers out there, setting up Debug-gym is pretty smooth:
conda create -n debug-gym python=3.12
conda activate debug-gym
pip install debug-gym
Want to run it locally? You can use Docker as a better and safer option. The whole thing’s open-source on GitHub, and Microsoft even threw in example benchmarks and minimal buggy programs to try.
Teaching AI to Think Like a Developer
One of the coolest ideas behind Debug-gym by Microsoft Research is this: it teaches AI to think in steps. Instead of just guessing fixes based on past data, the agent learns to explore, test hypotheses, use tools and then fix. That’s huge because real-world coding isn’t about memorizing answers; it’s about solving new problems with limited info. That’s what Debug-gym is training AI to do.
Wrapping Up
Microsoft believes that interactive tools like debug-gym are essential for advancing AI coding capabilities. The ability to actively seek information – rather than just generating code based on static context – is key to building AI systems that can truly assist with complex software development tasks.
Debug-gym provides a standardized way to test and improve how AI models interact with code, going beyond simply generating or rewriting it. This could lead to more practical AI coding assistants that complement human developers rather than trying to replace them.
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