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Neural program embedding has shown potential in aiding the analysis oflarge-scale, complicated software. Newly proposed deep neural architecturespride themselves on learning program semantics rather than superficialsyntactic features. However, by considering the source code only, the vastmajority of neural networks do not capture a deep, precise representation ofprogram semantics. In this paper, we present \dypro, a novel deep neuralnetwork that learns from program execution traces. Compared to the priordynamic models, not only is \dypro capable of generalizing across multipleexecutions for learning a program's dynamic semantics in its entirety, but\dypro is also more efficient when dealing with programs yielding longexecution traces. For evaluation, we task \dypro with semantic classification(i.e. categorizing programs based on their semantics) and compared it againsttwo prominent static models: Gated Graph Neural Network and TreeLSTM. We findthat \dypro achieves the highest prediction accuracy among all models. Tofurther reveal the capacity of all aforementioned deep neural architectures, weexamine if the models can learn to detect deeper semantic properties of aprogram. In particular given a task of recognizing loop invariants, we show\dypro beats all static models by a wide margin.

Learning Scalable and Precise Representation of Program Semantics