The goal of program synthesis is to automatically generate programs in a
particular language from corresponding specifications, e.g. input-output
behavior. Many current approaches achieve impressive results after training on
randomly generated I/O examples in limited domain-specific languages (DSLs), as
with string transformations in RobustFill. However, we empirically discover
that applying test input generation techniques for languages with control flow
and rich input space causes deep networks to generalize poorly to certain data
distributions; to correct this, we propose a new methodology for controlling
and evaluating the bias of synthetic data distributions over both programs and
specifications. We demonstrate, using the Karel DSL and a small Calculator DSL,
that training deep networks on these distributions leads to improved
cross-distribution generalization performance.

本文旨在探索自动生成指定语言中的程序，依据输入输出行为生成程序，我们提出了一种新的方法来控制和评估合成数据分布的偏差，通过在 Karel 和小型计算器 DSL 上的实验表明，使用这些分布训练深度网络可以提高跨分布的泛化性能。