一句话：提出按程序执行路径学习复杂度分配采样比例，并用程序分析判定复杂度以训练神经网络程序代理模型。
程序代理模型（surrogate）是对程序可观测行为子集的近似，被广泛用于解决各类软件开发难题。训练神经代理模型需要从程序输入示例中采样数据，但如何选取训练数据仍是关键挑战。本文提出一种面向神经网络程序代理模型的数据集采样方法：首先根据学习程序不同执行路径对应代理模型的复杂度，刻画应从输入空间各区域采样的数据比例；随后给出一种程序分析，用于判定程序中不同路径的学习复杂度，从而指导采样。作者在一系列真实程序上评估该方法，结果表明复杂度引导采样在经验上提升了代理模型的准确率。
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.
@article{paperbot1864,
title = {Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs},
author = {Alex Renda and Yi Ding and Michael Carbin},
journal = {Proceedings of the ACM on Programming Languages},
volume = {7},
number = {OOPSLA2},
year = {2023},
doi = {10.1145/3622856}
}