eprintid: 10126812
rev_number: 14
eprint_status: archive
userid: 608
dir: disk0/10/12/68/12
datestamp: 2021-04-28 08:19:14
lastmod: 2021-10-11 22:45:52
status_changed: 2021-04-28 08:19:14
type: article
metadata_visibility: show
creators_name: Blot, A
creators_name: Petke, J
title: Empirical Comparison of Search Heuristics for Genetic Improvement of Software
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Software,
Search problems,
Genetic programming,
Statistics,
Sociology,
Computer bugs,
Navigation, Genetic Improvement,
Search-Based Software Engineering,
Stochastic Local Search,
Genetic Programming.
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Genetic improvement uses automated search to improve existing software. It has been successfully used to optimise various program properties, such as runtime or energy consumption, as well as for the purpose of bug fixing. Genetic improvement typically navigates a space of thousands of patches in search for the program mutation that best improves the desired software property. While genetic programming has been dominantly used as the search strategy, more recently other search strategies, such as local search, have been tried. It is, however, still unclear which strategy is the most effective and efficient. In this paper, we conduct an in-depth empirical comparison of a total of 18 search processes using a set of 8 improvement scenarios. Additionally, we also provide new genetic improvement benchmarks and we report on new software patches found. Our results show that, overall, local search approaches achieve better effectiveness and efficiency than genetic programming approaches. Moreover, improvements were found in all scenarios (between 15% and 68%). A replication package can be found online: https://github.com/bloa/tevc _2020 artefact.
date: 2021-03-31
date_type: published
official_url: http://dx.doi.org/10.1109/TEVC.2021.3070271
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1860208
doi: 10.1109/TEVC.2021.3070271
lyricists_name: Blot, Aymeric
lyricists_name: Petke, Justyna
lyricists_id: ABLOT72
lyricists_id: JPETK66
actors_name: Petke, Justyna
actors_id: JPETK66
actors_role: owner
full_text_status: public
publication: IEEE Transactions on Evolutionary Computation
citation:        Blot, A;    Petke, J;      (2021)    Empirical Comparison of Search Heuristics for Genetic Improvement of Software.                   IEEE Transactions on Evolutionary Computation        10.1109/TEVC.2021.3070271 <https://doi.org/10.1109/TEVC.2021.3070271>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10126812/1/main.pdf