Shrinking the Inductive Programming Search Space with Instruction Subsets

TL;DR

This paper introduces a method to reduce the search space in inductive programming by predicting relevant instruction subsets, enabling more efficient and scalable program synthesis.

Contribution

It presents a novel instruction co-occurrence model that partitions the search space into intersecting subsets, improving scalability and parallel exploration in inductive programming.

Findings

High percentage of cases correctly predict instruction subsets

Significant reduction in search space size, often by orders of magnitude

Number of subsets needed does not grow linearly with code complexity

Abstract

Inductive programming frequently relies on some form of search in order to identify candidate solutions. However, the size of the search space limits the use of inductive programming to the production of relatively small programs. If we could somehow correctly predict the subset of instructions required for a given problem then inductive programming would be more tractable. We will show that this can be achieved in a high percentage of cases. This paper presents a novel model of programming language instruction co-occurrence that was built to support search space partitioning in the Zoea distributed inductive programming system. This consists of a collection of intersecting instruction subsets derived from a large sample of open source code. Using the approach different parts of the search space can be explored in parallel. The number of subsets required does not grow linearly with the quantity of code used to produce them and a manageable number of subsets is sufficient to cover a high percentage of unseen code. This approach also significantly reduces the overall size of the search space - often by many orders of magnitude.