Reference : Selecting fault revealing mutants

	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/42520

Title :	Selecting fault revealing mutants
Language :	English
Author, co-author :	Titcheu Chekam, Thierry [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Papadakis, Mike [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
	Bissyande, Tegawendé François D Assise [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Le Traon, Yves [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
	Sen, Koushik [Berkeley University of California - UC Berkeley]
Publication date :	18-Dec-2019
Journal title :	Empirical Software Engineering
Publisher :	Springer
Pages :	1-54
Peer reviewed :	Yes (verified by ORBilu)
Audience :	International
ISSN :	1382-3256
e-ISSN :	1573-7616
Country :	US
Keywords :	[en] Mutation testing ; Machine learning ; Mutant selection ; Mutant prioritization
Abstract :	[en] Mutant selection refers to the problem of choosing, among a large number of mutants, the (few) ones that should be used by the testers. In view of this, we investigate the problem of selecting the fault revealing mutants, i.e., the mutants that are killable and lead to test cases that uncover unknown program faults. We formulate two variants of this problem: the fault revealing mutant selection and the fault revealing mutant prioritization. We argue and show that these problems can be tackled through a set of ‘static’ program features and propose a machine learning approach, named FaRM, that learns to select and rank killable and fault revealing mutants. Experimental results involving 1,692 real faults show the practical benefits of our approach in both examined problems. Our results show that FaRM achieves a good trade-off between application cost and effectiveness (measured in terms of faults revealed). We also show that FaRM outperforms all the existing mutant selection methods, i.e., the random mutant sampling, the selective mutation and defect prediction (mutating the code areas pointed by defect prediction). In particular, our results show that with respect to mutant selection, our approach reveals 23% to 34% more faults than any of the baseline methods, while, with respect to mutant prioritization, it achieves higher average percentage of revealed faults with a median difference between 4% and 9% (from the random mutant orderings).
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Security Design and Validation Research Group (SerVal)
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	CODEMATES
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/42520
DOI :	10.1007/s10664-019-09778-7
Other URL :	https://arxiv.org/abs/1803.07901
FnR project :	FnR ; FNR11686509 > Michail Papadakis > CODEMATES > COntinuous DEvelopment with Mutation Analysis and TESting > 01/09/2018 > 31/08/2021 > 2017

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