Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/27482
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: An Empirical Study of Meta- and Hyper-Heuristic Search for Multi-Objective Release Planning
Author(s): Zhang, Yuanyuan
Harman, Mark
Ochoa, Gabriela
Ruhe, Guenther
Brinkkemper, Sjaak
Keywords: Software engineering
algorithms
experimentation
measurement
strategic release planning
meta-heuristics
hyper-heuristics
Issue Date: 5-Jun-2018
Citation: Zhang Y, Harman M, Ochoa G, Ruhe G & Brinkkemper S (2018) An Empirical Study of Meta- and Hyper-Heuristic Search for Multi-Objective Release Planning, ACM Transactions on Software Engineering and Methodology, 27 (1), Art. No.: 3. https://doi.org/10.1145/3196831.
DAASE: Dynamic Adaptive Automated Software Engineering
EP/J017515/1
Abstract: A variety of meta-heuristic search algorithms have been introduced for optimising software release planning. However, there has been no comprehensive empirical study of different search algorithms across multiple different real-world datasets. In this article, we present an empirical study of global, local, and hybrid meta- and hyper-heuristic search-based algorithms on 10 real-world datasets. We find that the hyper-heuristics are particularly effective. For example, the hyper-heuristic genetic algorithm significantly outperformed the other six approaches (and with high effect size) for solution quality 85% of the time, and was also faster than all others 70% of the time. Furthermore, correlation analysis reveals that it scales well as the number of requirements increases.
DOI Link: 10.1145/3196831
Rights: © ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Software Engineering and Methodology, Volume 27, 1 (2018) http://doi.acm.org/10.1145/3196831

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