Programming by example is the problem of synthesizing a program from a small set of input / output pairs. Recent works applying machine learning methods to this task show promise, but are typically reliant on generating synthetic examples for training. A particular challenge lies in generating meaningful sets of inputs and outputs, which well-characterize a given program and accurately demonstrate its behavior. Where examples used for testing are generated by the same method as training data then the performance of a model may be partly reliant on this similarity. In this paper we introduce a novel approach using an SMT solver to synthesize inputs which cover a diverse set of behaviors for a given program. We carry out a case study comparing this method to existing synthetic data generation procedures in the literature, and find that data generated using our approach improves both the discriminatory power of example sets and the ability of trained machine learning models to generalize to unfamiliar data.

本论文介绍了一种用SMT求解器合成覆盖给定程序多样化行为的输入的新方法，并通过与现有合成数据生成程序的比较进行了案例研究，发现使用该方法生成的数据既能提高样例集的差异性，也能提高训练后的机器学习模型对未知数据的推广能力。

通过示例生成神经编程数据