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

Data Generation for Neural Programming by Example