@conference {35,
	title = {Empirical Evaluation of Pareto Efficient Multi-objective Regression Test Case Prioritisation},
	booktitle = {International Symposium on Software Testing and Analysis (ISSTA{\textquoteright}15)},
	year = {2015},
	publisher = {ACM},
	organization = {ACM},
	address = {Baltimore, MD, USA},
	abstract = {The aim of test case prioritisation is to determine an ordering of test cases that maximises the likelihood of early fault revelation. Previous prioritisation techniques have tended to be single objective, for which the additional greedy algorithm is the current state-of-the-art. Unlike test suite minimisation, multi objective test case prioritisation has not been thoroughly evaluated. This paper presents an extensive empirical study of the effectiveness of multi objective test case prioritisation, evaluating it on multiple versions of five widely-used benchmark programs and a much larger real world system of over 1 million lines of code. The paper also presents a lossless coverage compaction algorithm that dramatically scales the performance of all algorithms studied by between 2 and 4 orders of magnitude, making prioritisation practical for even very demanding problems.},
	keywords = {additional greedy algorithm, coverage compaction, multi-objective evolutionary algo- rithm, Test case prioritization},
	author = {Michael G. Epitropakis and Shin Yoo and Mark Harman and Edmund K. Burke}
}
@article {33,
	title = {Pareto Efficient Multi-Objective Regression Test Suite Prioritisation},
	year = {2014},
	month = {04/2014},
	pages = {1--16},
	institution = {Department of Computer Science, University College London},
	address = {Gower Street, London},
	abstract = {Test suite prioritisation seeks a test case ordering that maximises the likelihood of early fault revelation. Previous prioritisation techniques have tended to be single objective, for which the additional greedy algorithm is the current state-of-the-art. We study multi objective test suite prioritisation, evaluating it on multiple versions of five widely-used benchmark programs and a much larger real world system of over 1MLoC. Our multi objective algorithms find faults significantly faster and with large effect size for 20 of the 22 versions. We also introduce a non-lossy coverage compact algorithm that dramatically scales the performance of all algorithms studied by between 2 and 4 orders of magnitude, making prioritisation practical for even very demanding problems.},
	issn = {RN/14/01},
	author = {Michael G. Epitropakis and Shin Yoo and Mark Harman and Edmund K. Burke}
}
@proceedings {29,
	title = {A Dynamic Archive Niching Differential Evolution Algorithm for Multimodal Optimization},
	year = {2013},
	month = {June},
	pages = {79-86},
	address = {Cancun, Mexico},
	abstract = {Highly multimodal landscapes with multiple local/global optima represent common characteristics in real-world applications. Many niching algorithms have been proposed in the literature which aim to search such landscapes in an attempt to locate as many global optima as possible. However, to locate and maintain a large number of global solutions, these algorithms are substantially influenced by their parameter values, such as a large population size. Here, we propose a new niching Differential Evolution algorithm that attempts to overcome the population size influence and produce good performance almost independently of its population size. To this end, we incorporate two mechanisms into the algorithm: a control parameter adaptation technique and an external dynamic archive along with a reinitialization mechanism. The first mechanism is designed to efficiently adapt the control parameters of the algorithm, whilst the second one is responsible for enabling the algorithm to investigate unexplored regions of the search space and simultaneously keep the best solutions found by the algorithm. The proposed approach is compared with two Differential Evolution variants on a recently proposed benchmark suite. Empirical results indicate that the proposed niching algorithm is competitive and very promising. It exhibits a robust and stable behavior, whilst the incorporation of the dynamic archive seems to tackle the population size influence effectively. Moreover, it alleviates the problem of having to fine-tune the population size parameter in a niching algorithm.},
	doi = {10.1109/CEC.2013.6557556},
	url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6557556},
	author = {M. G. Epitropakis and Xiaodong Li and Edmund K. Burke}
}
