@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 {32,
	title = {Gen-O-Fix: An embeddable framework for Dynamic Adaptive Genetic Improvement Programming},
	number = {CSM-195},
	year = {2014},
	month = {01/2014},
	pages = {1-12},
	institution = {Computing Science and Mathematics, University of Stirling},
	address = {Stirling FK9 4LA, Scotland},
	abstract = {Genetic Improvement Programming (GIP) is concerned with automating the burden of software maintenance, the most costly phase of the software lifecycle. We describe Gen-O-Fix, a GIP framework which allows a software system hosted on the Java Virtual Machine to be continually improved (e.g. make better predictions; pass more regression tests; reduce power consumption). It is the first exemplar of a dynamic adaptive GIP framework, i.e. it can improve a system as it runs. It is written in the Scala programming language and uses reflection to yield source-to-source transformation. One of the design goals for Gen-O-Fix was to create a tool that is user-centric rather than researcher-centric: the end-user is required only to provide a measure of system quality and the URL of the source code to be improved. We discuss potential applications to predictive, embedded and high-performance systems.},
	author = {Jerry Swan and Michael G. Epitropakis and John R. Woodward}
}
@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}
}
