  Code semantics similarity can be used for many tasks such as code
recommendation, automated software defect correction, and clone detection. Yet,
the accuracy of such systems has not yet reached a level of general purpose
reliability. To help address this, we present Machine Inferred Code Similarity
(MISIM), a neural code semantics similarity system consisting of two core
components: (i)MISIM uses a novel context-aware semantics structure, which was
purpose-built to lift semantics from code syntax; (ii)MISIM uses an extensible
neural code similarity scoring algorithm, which can be used for various neural
network architectures with learned parameters. We compare MISIM to four
state-of-the-art systems, including two additional hand-customized models, over
328K programs consisting of over 18 million lines of code. Our experiments show
that MISIM has 8.08% better accuracy (using MAP@R) compared to the next best
performing system.
