Deep learning model for learning program embeddings

A computer comprising:

• a processor;
• and
• a computer-readable medium comprising code, executable by the processor, for implementing a method comprising:
• receiving a plurality of execution traces of a program, each execution trace comprising a plurality of variable values, and wherein each execution trace of the program includes a semantic label describing a processing function of the program;
• encoding, by a first recurrent neural network, the plurality of variable values to generate a plurality of program states for each execution trace;
• determining, by a bi-directional recurrent neural network, a reduced set of program states for each execution trace from the plurality of program states,
• wherein determining the reduced set of program states for each execution trace from the plurality of program states comprises:
• computing a forward sequence from the plurality of program states;
• computing a backward sequence from the plurality of program states;
• for each program state in the plurality of program states:
• determining a forward context vector from the forward sequence;
• determining a backward context vector from the backward sequence;
• applying a multi-layer perceptron to predict a necessity of each program state;
• and
• determining, based on a result of the multi-layer perceptron,
• whether to include the program state in the reduced set of program states;
• encoding, by a second recurrent neural network, the reduced set of program states to generate a plurality of executions for the program;
• pooling the plurality of executions to generate a program embedding;
• predicting semantics of the program using the program embedding in order to classify the program, wherein the predicted semantics include one or more semantic labels;
• and
• using the predicted semantics of the program to perform a compiler optimization task, wherein the method is optimized to minimize a cross-entropy loss and minimize a number of program states in the reduced set of program states.