Optimal Data Sampling for Training Neural Surrogates of Programs

Programmers and researchers are increasingly developing surrogates of programs, models of a subset of the observable behavior of a given program, to solve a variety of software development challenges. Programmers train surrogates from measurements of the behavior of a program on a dataset of input examples. We present a methodology for optimally sampling datasets to train neural network based surrogates of programs. We first characterize the optimal proportion of data to sample from each path in a program based on the complexity of learning the path. We next provide a program analysis to determine the complexity of different paths in a program. We evaluate these results on a large-scale graphics program, demonstrating that theoretically optimal sampling results in empirical improvements in accuracy.