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Injecting Shortcuts for Faster Running Java Code

Brownlee, AEI; Petke, J; Rasburn, AF; (2020) Injecting Shortcuts for Faster Running Java Code. In: Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE: Glasgow, UK. Green open access

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Abstract

Genetic Improvement of software applies search methods to existing software to improve the target program in some way. Impressive results have been achieved, including substantial speedups, using simple operations that replace, swap and delete lines or statements within the code. Often this is achieved by specialising code, removing parts that are unnecessary for particular use-cases. Previous work has shown that there is a great deal of potential in targeting more specialised operations that modify the code to achieve the same functionality in a different way. We propose six new edit types for Genetic Improvement of Java software, based on the insertion of break, continue and return statements. The idea is to add shortcuts that allow parts of the program to be skipped in order to speed it up. 10000 randomlygenerated instances of each edit were applied to three opensource applications taken from GitHub. The key findings are: (1) compilation rates for inserted statements without surrounding “if” statements are 1.3-18.3%; (2) edits where the inserted statement is embedded within an “if” have compilation rates of 3.2-55.8%; (3) of those that compiled, all 6 edits have a high rate of passing tests (Neutral Variant Rate), >60% in all but one case, and so have the potential to be performance improving edits. Finally, a preliminary experiment based on local search shows how these edits might be used in practice.

Type: Proceedings paper
Title: Injecting Shortcuts for Faster Running Java Code
Event: 2020 IEEE Congress on Evolutionary Computation (CEC)
ISBN-13: 978-1-7281-6929-3
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/CEC48606.2020.9185708
Publisher version: https://doi.org/10.1109/CEC48606.2020.9185708
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Genetic Improvement, GI, Search-Based Software Engineering, SBSE
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/10113443
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