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With the rise of machine learning, there is a great deal of interest intreating programs as data to be fed to learning algorithms. However, programsdo not start off in a form that is immediately amenable to most off-the-shelflearning techniques. Instead, it is necessary to transform the program to asuitable representation before a learning technique can be applied.  In this paper, we use abstractions of traces obtained from symbolic executionof a program as a representation for learning word embeddings. We trained avariety of word embeddings under hundreds of parameterizations, and evaluatedeach learned embedding on a suite of different tasks. In our evaluation, weobtain 93% top-1 accuracy on a benchmark consisting of over 19,000 API-usageanalogies extracted from the Linux kernel. In addition, we show that embeddingslearned from (mainly) semantic abstractions provide nearly triple the accuracyof those learned from (mainly) syntactic abstractions.

Code Vectors: Understanding Programs Through Embedded Abstracted Symbolic Traces