Origami: (un)folding the abstraction of recursion schemes for program synthesis

• URL: http://arxiv.org/abs/2402.13828v2
• Date: Tue, 27 Feb 2024 00:13:38 GMT
• Title: Origami: (un)folding the abstraction of recursion schemes for program synthesis
• Authors: Matheus Campos Fernandes, Fabricio Olivetti de Franca, Emilio Francesquini
• Abstract summary: Genetic Programming searches for a correct program that satisfies the input specification. One particular challenge is how to handle loops and recursion avoiding programs that never terminate. Recursion Schemes generalize the combination of data production and consumption.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Program synthesis with Genetic Programming searches for a correct program that satisfies the input specification, which is usually provided as input-output examples. One particular challenge is how to effectively handle loops and recursion avoiding programs that never terminate. A helpful abstraction that can alleviate this problem is the employment of Recursion Schemes that generalize the combination of data production and consumption. Recursion Schemes are very powerful as they allow the construction of programs that can summarize data, create sequences, and perform advanced calculations. The main advantage of writing a program using Recursion Schemes is that the programs are composed of well defined templates with only a few parts that need to be synthesized. In this paper we make an initial study of the benefits of using program synthesis with fold and unfold templates, and outline some preliminary experimental results. To highlight the advantages and disadvantages of this approach, we manually solved the entire GPSB benchmark using recursion schemes, highlighting the parts that should be evolved compared to alternative implementations. We noticed that, once the choice of which recursion scheme is made, the synthesis process can be simplified as each of the missing parts of the template are reduced to simpler functions, which are further constrained by their own input and output types.

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