MP-CodeCheck: Evolving Logical Expression Code Anomaly Learning with Iterative Self-Supervision

Abstract

Machine programming (MP) is concerned with automating software development. According to studies, software engineers spend upwards of 50% of their development time debugging software. To help accelerate debugging, we present MP-CodeCheck (MPCC). MPCC is an MP system that attempts to identify anomalous code patterns within logical program expressions. In designing MPCC, we developed two novel programming language representations, the formations of which are critical in its ability to exhaustively and efficiently process the billions of lines of code that are used in its self-supervised training.To quantify MPCC's performance, we compare it against ControlFlag, a state-of-the-art self-supervised code anomaly detection system; we find that MPCC is more spatially and temporally efficient. We demonstrate MPCC's anomalous code detection capabilities by exercising it on a variety of open-source GitHub repositories and one proprietary code base. We also provide a brief qualitative study on some of the different classes of code anomalies that MPCC can detect to provide an abbreviated insight into its capabilities. 1 CCS Concepts: • Computing methodologies → Learning settings; • Software and its engineering → General programming languages; Semantics.