Article (Scientific journals)
Selecting fault revealing mutants
TITCHEU CHEKAM, Thierry; PAPADAKIS, Mike; BISSYANDE, Tegawendé François D Assise et al.
2019In Empirical Software Engineering, p. 1-54
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Keywords :
Mutation testing; Machine learning; Mutant selection; Mutant prioritization
Abstract :
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 center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Security Design and Validation Research Group (SerVal)
Disciplines :
Computer science
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
External co-authors :
yes
Language :
English
Title :
Selecting fault revealing mutants
Publication date :
18 December 2019
Journal title :
Empirical Software Engineering
ISSN :
1573-7616
Publisher :
Springer, United States
Pages :
1-54
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Security, Reliability and Trust
FnR Project :
FNR11686509 - Continuous Development With Mutation Analysis And Testing, 2017 (01/09/2018-31/08/2021) - Michail Papadakis
Name of the research project :
CODEMATES
Funders :
FNR - Fonds National de la Recherche [LU]
Available on ORBilu :
since 17 February 2020

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