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With the recent success of embeddings in natural language processing,research has been conducted into applying similar methods to code analysis.Most works attempt to process the code directly or use a syntactic treerepresentation, treating it like sentences written in a natural language.However, none of the existing methods are sufficient to comprehend programsemantics robustly, due to structural features such as function calls,branching, and interchangeable order of statements. In this paper, we propose anovel processing technique to learn code semantics, and apply it to a varietyof program analysis tasks. In particular, we stipulate that a robustdistributional hypothesis of code applies to both human- and machine-generatedprograms. Following this hypothesis, we define an embedding space, inst2vec,based on an Intermediate Representation (IR) of the code that is independent ofthe source programming language. We provide a novel definition of contextualflow for this IR, leveraging both the underlying data- and control-flow of theprogram. We then analyze the embeddings qualitatively using analogies andclustering, and evaluate the learned representation on three differenthigh-level tasks. We show that even without fine-tuning, a single RNNarchitecture and fixed inst2vec embeddings outperform specialized approachesfor performance prediction (compute device mapping, optimal thread coarsening);and algorithm classification from raw code (104 classes), where we set a newstate-of-the-art.

Neural Code Comprehension: A Learnable Representation of Code Semantics