Reference : Selecting fault revealing mutants
Scientific journals : Article
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/42520
Selecting fault revealing mutants
English
Titcheu Chekam, Thierry mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Papadakis, Mike mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
Bissyande, Tegawendé François D Assise mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Le Traon, Yves mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
Sen, Koushik mailto [Berkeley University of California - UC Berkeley]
18-Dec-2019
Empirical Software Engineering
Springer
1-54
Yes (verified by ORBilu)
International
1382-3256
1573-7616
US
[en] Mutation testing ; Machine learning ; Mutant selection ; Mutant prioritization
[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).
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Security Design and Validation Research Group (SerVal)
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/42520
10.1007/s10664-019-09778-7
https://arxiv.org/abs/1803.07901
FnR ; FNR11278802 > Titcheu Chekam Thierry > > Tailoring Automated Software Techniques for Real World and Large Scale Software Applications > 01/03/2016 > 31/01/2020 > 2016

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
TitcheuChekam2019_Article_SelectingFaultRevealingMutants.pdfPublisher postprint3.28 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.