Understanding dynamic program behavior is critical in many stages of the software development lifecycle, for purposes as diverse as optimization, debugging, testing, and security. This paper focuses on the problem of predicting dynamic program behavior statically. We introduce a novel technique to statically identify hot paths that leverages emerging deep learning techniques to take advantage of their ability to learn subtle, complex relationships between sequences of inputs.
Stephen A. Zekany,
Daniel Rings,
Nathan Harada,
Michael A. Laurenzano,
Lingjia Tang,
Jason Mars