Software debugging has been shown to utilize upwards of half of developers’ time. Yet, machine programming (MP), the field concerned with the automation of software (and hardware) development, has recently made strides in both research and production-quality automated debugging systems. In this paper we present ControlFlag, an MP system that aims to improve debugging. ControlFlag does this by attempting to detect idiosyncratic pattern violations in software control structures. In some cases, these pattern violations can result in software defects. ControlFlag also suggests possible corrections in the event a true error is detected. A novelty of ControlFlag is that it is entirely self-supervised. That is, it requires no labels on training data to learn potential idiosyncratic programming pattern violations. We present ControlFlag’s design and provide an abbreviated experimental evaluation and analysis of its efficacy in identifying potential programming errors in production-quality software. We close with a discussion of extensions of ControlFlag to increase its generalizability.