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Data Generation for Neural Programming by Example

Clymo, J; Manukian, H; Fijalkow, NS; Gascon, A; Paige, B; (2020) Data Generation for Neural Programming by Example. In: Chiappa, S and Calandra, R, (eds.) Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. (pp. pp. 3450-3458). Proceedings of Machine Learning Research (PMLR): Online conference. Green open access

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Abstract

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.

Type: Proceedings paper
Title: Data Generation for Neural Programming by Example
Event: 23rd International Conference on Artificial Intelligence and Statistics (AISTATS)
Location: ELECTR NETWORK
Dates: 26 August 2020 - 28 August 2020
Open access status: An open access version is available from UCL Discovery
Publisher version: http://proceedings.mlr.press/v108/clymo20a.html
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10115928
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