research-article

An Empirical Study of Meta- and Hyper-Heuristic Search for Multi-Objective Release Planning

Authors:

Yuanyuan Zhang,

Gabriela Ochoa,

Sjaak BrinkkemperAuthors Info & Claims

ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 27, Issue 1

Article No.: 3, Pages 1 - 32

https://doi.org/10.1145/3196831

Published: 05 June 2018 Publication History

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.

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cover image ACM Transactions on Software Engineering and Methodology

ACM Transactions on Software Engineering and Methodology Volume 27, Issue 1

January 2018

167 pages

ISSN:1049-331X

EISSN:1557-7392

DOI:10.1145/3208361

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Published: 05 June 2018

Accepted: 01 March 2018

Revised: 01 February 2018

Received: 01 September 2015

Published in TOSEM Volume 27, Issue 1

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