Turaco: Complexity-Guided 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 e...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings of ACM on programming languages 2023-10, Vol.7 (OOPSLA2), p.1648-1676, Article 280
Main Authors: Renda, Alex, Ding, Yi, Carbin, Michael
Format: Article
Language:English
Subjects:
Automatic programming
Computing methodologies
Machine learning
Software and its engineering
Software evolution
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. A key challenge of surrogate construction is determining what training data to use to train a surrogate of a given program. We present a methodology for sampling datasets to train neural-network-based surrogates of programs. We first characterize the proportion of data to sample from each region of a program's input space (corresponding to different execution paths of the program) based on the complexity of learning a surrogate of the corresponding execution path. We next provide a program analysis to determine the complexity of different paths in a program. We evaluate these results on a range of real-world programs, demonstrating that complexity-guided sampling results in empirical improvements in accuracy.
ISSN:2475-1421
2475-1421
DOI:10.1145/3622856