  Machine learning-based program analyses have recently shown the promise of
integrating formal and probabilistic reasoning towards aiding software
development. However, in the absence of large annotated corpora, training these
analyses is challenging. Towards addressing this, we present BugLab, an
approach for self-supervised learning of bug detection and repair. BugLab
co-trains two models: (1) a detector model that learns to detect and repair
bugs in code, (2) a selector model that learns to create buggy code for the
detector to use as training data. A Python implementation of BugLab improves by
up to 30% upon baseline methods on a test dataset of 2374 real-life bugs and
finds 19 previously unknown bugs in open-source software.
