  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.
