ControlFlag: A Self-Supervised Idiosyncratic Pattern Detection System for Software Control Structures
This addresses the time-consuming debugging process for software developers, though it appears incremental as it builds on existing machine programming automation efforts.
The paper tackles the problem of software debugging by introducing ControlFlag, a self-supervised system that detects idiosyncratic pattern violations in control structures and suggests corrections, with evidence of finding and fixing an anomaly in CURL.
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, a self-supervised MP system that aims to improve debugging by attempting to detect idiosyncratic pattern violations in software control structures. ControlFlag also suggests possible corrections in the event an anomalous pattern is detected. We present ControlFlag's design and provide an experimental evaluation and analysis of its efficacy in identifying potential programming errors in production-quality software. As a first concrete evidence towards improving software quality, ControlFlag has already found an anomaly in CURL that has been acknowledged and fixed by its developers. We also discuss future extensions of ControlFlag.