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

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

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.

Keywords
Software engineering; algorithms; experimentation; measurement; strategic release planning; meta-heuristics; hyper-heuristics;

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

People

Professor Gabriela Ochoa

Professor, Computing Science