Scaling Genetic Programming for Source Code Modification

TL;DR

This paper explores enhancing the scalability of Genetic Programming for source code modification by integrating Software Engineering principles like modularity, granularity, and localization of change into the algorithm.

Contribution

It introduces a novel approach that reformulates Software Engineering concepts as mechanisms within Genetic Programming to improve scalability for real-world software tasks.

Findings

Proposes a new framework combining Software Engineering principles with Genetic Programming.

Addresses scalability issues in applying GP to large source code bases.

Lays groundwork for more efficient automated code modification techniques.

Abstract

In Search Based Software Engineering, Genetic Programming has been used for bug fixing, performance improvement and parallelisation of programs through the modification of source code. Where an evolutionary computation algorithm, such as Genetic Programming, is to be applied to similar code manipulation tasks, the complexity and size of source code for real-world software poses a scalability problem. To address this, we intend to inspect how the Software Engineering concepts of modularity, granularity and localisation of change can be reformulated as additional mechanisms within a Genetic Programming algorithm.