Infrared: A Meta Bug Detector
This addresses the challenge of creating effective bug detectors for software developers with less training data, though it appears incremental as it builds on existing learning-based methods.
The paper tackles the problem of data-hungry and hard-to-train learning-based bug detectors by proposing a meta bug detection approach that is bug-type generic, self-explainable, and sample-efficient, and it significantly outperforms baselines like Facebook Infer and FICS in catching various bugs.
The recent breakthroughs in deep learning methods have sparked a wave of interest in learning-based bug detectors. Compared to the traditional static analysis tools, these bug detectors are directly learned from data, thus, easier to create. On the other hand, they are difficult to train, requiring a large amount of data which is not readily available. In this paper, we propose a new approach, called meta bug detection, which offers three crucial advantages over existing learning-based bug detectors: bug-type generic (i.e., capable of catching the types of bugs that are totally unobserved during training), self-explainable (i.e., capable of explaining its own prediction without any external interpretability methods) and sample efficient (i.e., requiring substantially less training data than standard bug detectors). Our extensive evaluation shows our meta bug detector (MBD) is effective in catching a variety of bugs including null pointer dereference, array index out-of-bound, file handle leak, and even data races in concurrent programs; in the process MBD also significantly outperforms several noteworthy baselines including Facebook Infer, a prominent static analysis tool, and FICS, the latest anomaly detection method.