Microsoft supports researchers, non-profits, and organizations who are using technology and AI to improve the world across multiple disciplines: the environment, accessibility, humanitarian issues, cultural heritage, and health. Recently I discovered Microsoft's BugLab, which uses two competing models that learn by playing a "hide and seek" game that is broadly inspired by generative adversarial networks (GAN). Given some existing code, presumed to be correct, a bug selector model decides if it should introduce a bug, where to introduce it, and its exact form (e.g., replace a specific " " with a "-"). Given the selector choice, the code is edited to introduce the bug. Then, another model, the bug detector, tries to determine if a bug was introduced in the code, and if so, locate it, and fix it.

Writing code is only the first step in creating something. Combing through your code for bugs and fixing them is time-consuming and often takes longer than anticipated, but is an essential step nonetheless. If only there was a way to automatically fix bugs that goes beyond syntax errors and truly understands the intentions behind your code. Recently, Microsoft developed an AI capable of detecting and fixing bugs in code using deep learning. But how did this piece of revolutionary tech come to exist, and how does it work?

Microsoft researchers have been working on deep learning model that was trained to find software bugs without any real-world bugs to learn from. While there are dozens of tools available for static analysis of code in various languages to find security flaws, researchers have been exploring techniques that use machine learning to improve the ability to both detect flaws and fix them. That's because finding and fixing bugs in code can be hard and costly, even when using AI to find them. Every remote worker should consider a virtual private network to stay safe online. Researchers Microsoft Research Cambridge, UK have detailed their work on BugLab, a Python implementation of "an approach for self-supervised learning of bug detection and repair".