# Data Generation for Neural Programming by Example

arXiv: https://arxiv.org/abs/1911.02624 · HTML: https://mlsanity.com/paper/1911.02624/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [Latent Execution for Neural Program Synthesis](https://mlsanity.com/paper/2107.00101/) (2021)
- [Scaling Neural Program Synthesis with Distribution-based Search](https://mlsanity.com/paper/2110.12485/) (2021)
- [NeuroSynt: A Neuro-symbolic Portfolio Solver for Reactive Synthesis](https://mlsanity.com/paper/2401.12131/) (2024)

## Abstract (arXiv)

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.