Microsoft's New AI Debugging Tool

Microsoft researchers have developed an Artificial Intelligence (AI) solution that they believe can help programmers debug their applications faster and more accurately. Microsoft’s concept is to develop an  AI that can automatically find and repair bugs. Called BugLab, the AI is based on a “hide and seek” game model, and works in a fashion similar to how the Generative Adversarial Networks (GAN) are created.

Microsoft researchers Miltos Allamanir and Marc Brockschmidt explained in a blog how they created two networks and pitted them against each other, in the same way as the game of hide and seek.

One network is designed to create bugs, both big and small, into existing code, while the other is created to find them. As the game goes on, and both “participants” get better at it, the AI comes to a point where it’s good enough to identify bugs hidden in actual code. 

The two models were trained jointly, without labeled data, in a self-supervised way, over “millions of code snippets”, the researchers explained. 

Even though the idea was to create a program that can identify arbitrarily complex bugs, these are still “outside the reach of modern AI methods”, the researchers claim. Instead, they focused on commonly appearing bugs, such as incorrect comparisons, incorrect Boolean operators, variable misuses, and similar bugs. 

Testing was done on Python and after training the app, it was tested for real. “To measure performance, we manually annotate a small dataset of bugs from packages in the Python Package Index with such bugs and show that models trained with our “hide-and-seek” method are up to 30% better compared to other alternatives, e.g., detectors trained with randomly inserted bugs,” the blog added. 

The results are promising with 26% of bugs being found and fixed automatically. Significantly, among the bugs detected there were 19 previously unknown. However, there were numerous many false positives, leading the researchers to conclude that a lot more AI training is needed before their model could be practically deployed.

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