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A Hyper-heuristic for Multi-objective Integration and Test Ordering in Google Guava

Guizzo, G; Bazargani, M; Paixao, M; Drake, JH; (2017) A Hyper-heuristic for Multi-objective Integration and Test Ordering in Google Guava. In: Menzies, T and Petke, J, (eds.) Search Based Software Engineering: 9th International Symposium, SSBSE 2017, Paderborn, Germany, September 9-11, 2017, Proceedings. (pp. pp. 168-174). Springer: Padernborn, Germany. Green open access

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Abstract

Integration testing seeks to find communication problems between different units of a software system. As the order in which units are considered can impact the overall effort required to perform integration testing, deciding an appropriate sequence to integrate and test units is vital. Here we apply a multi-objective hyper-heuristic set within an NSGA-II framework to the Integration and Test Order Problem (ITO) for Google Guava, a set of open-source common libraries for Java. Our results show that an NSGA-II based hyper-heuristic employing a simplified version of Choice Function heuristic selection, outperforms standard NSGA-II for this problem.

Type: Proceedings paper
Title: A Hyper-heuristic for Multi-objective Integration and Test Ordering in Google Guava
Event: 9th International Symposium on Search Based Software Engineering (SSBSE)
Location: Paderborn, GERMANY
Dates: 09 September 2017 - 11 September 2017
ISBN-13: 978-3-319-66298-5
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-66299-2_15
Publisher version: https://doi.org/10.1007/978-3-319-66299-2_15
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.
UCL classification: 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/10076012
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