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ControlFlag: A Self-supervised Idiosyncratic Pattern Detection System for Software Control Structures

2020-11-06 22:19:05
Niranjan Hasabnis, Justin Gottschlich
arXiv_AI
     
Abstract

Software debugging has been shown to utilize upwards of 50% of developers' time. Machine programming, the field concerned with the automation of software (and hardware) development, has recently made progress in both research and production-quality automated debugging systems. In this paper, we present ControlFlag, a system that detects possible idiosyncratic violations in software control structures. 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 to learn about the potential idiosyncratic programming pattern violations. In addition to presenting ControlFlag's design, we also provide an abbreviated experimental evaluation.

Abstract (translated)

URL

https://arxiv.org/abs/2011.03616

PDF

https://arxiv.org/pdf/2011.03616.pdf

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