AI models from OpenAI, Anthropic, and other top AI labs are increasingly being used to assist with programming tasks. Google CEO Sundar Pichai said in October that 25% of new code at the company is generated by AI, and Meta CEO Mark Zuckerberg has expressed ambitions to widely deploy AI coding models within the social media giant.
Yet even some of the best models today struggle to resolve software bugs that wouldn’t trip up experienced devs.
A new study from Microsoft Research, Microsoft’s R&D division, reveals that models, including Anthropic’s Claude 3.7 Sonnet and OpenAI’s o3-mini, fail to debug many issues in a software development benchmark called SWE-bench Lite. The results are a sobering reminder that, despite bold pronouncements from companies like OpenAI, AI is still no match for human experts in domains such as coding.
The study’s co-authors tested nine different models as the backbone for a “single prompt-based agent” that had access to a number of debugging tools, including a Python debugger. They tasked this agent with solving a curated set of 300 software debugging tasks from SWE-bench Lite.
According to the co-authors, even when equipped with stronger and more recent models, their agent rarely completed more than half of the debugging tasks successfully. Claude 3.7 Sonnet had the highest average success rate (48.4%), followed by OpenAI’s o1 (30.2%) and o3-mini (22.1%).
Why the underwhelming performance? Some models struggled to use the debugging tools available to them and understand how different tools might help with different issues. The bigger problem, though, was data scarcity, according to the co-authors. They speculate that there’s not enough data representing “sequential decision-making processes” — that is, human debugging traces — in current models’ training data.
“We strongly believe that training or fine-tuning [models] can make them better interactive debuggers,” wrote the co-authors in their study. “However, this will require specialized data to fulfill such model training, for example, trajectory data that records agents interacting with a debugger to collect necessary information before suggesting a bug fix.”
The findings aren’t exactly shocking. Many studies have shown that code-generating AI tends to introduce security vulnerabilities and errors, owing to weaknesses in areas like the ability to understand programming logic. One recent evaluation of Devin, a popular AI coding tool, found that it could only complete three out of 20 programming tests.
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