Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MISIM: A Neural Code Semantics Similarity System Using the Context-Aware
  Semantics Structure   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.

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The categories of this paper are: gr-qc
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The categories of this paper are: q-alg
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The categories of this paper are: adap-org
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The categories of this paper are: funct-an
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The categories of this paper are: patt-sol
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: physics
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The categories of this paper are: math
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex