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).
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Eduard BaranovUniversité Catholique de Louvain, Belgium, Axel LegayUniversité Catholique de Louvain, Belgium, Kuldeep S. MeelNational University of Singapore, SingaporeDOI
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Giovani GuizzoUniversity College London, UK, Federica SarroUniversity College London, UK, Mark HarmanUniversity College London, UKDOI Pre-print
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Porfirio TramontanaDepartment of Electrical Engineering and Information Technologies, University of Naples Federico II, Italy, Domenico AmalfitanoUniversity of Naples Federico II, Nicola AmatucciDepartment of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Italy, Atif MemonApple Inc., Anna Rita FasolinoFederico II University of Naples
|08:07 - 08:08|
M. Ammar Ben KhadraTU Kaiserslautern, Germany, Dominik StoffelTU Kaiserslautern, Germany, Wolfgang KunzTU Kaiserslautern, GermanyDOI Pre-print Media Attached
|08:09 - 08:10|
Chu-Pan WongCarnegie Mellon University, USA, Jens MeinickeCarnegie Mellon University, USA, Leo ChenCarnegie Mellon University, USA, João P. DinizFederal University of Minas Gerais, Brazil, Christian KästnerCarnegie Mellon University, USA, Eduardo FigueiredoFederal University of Minas Gerais, BrazilDOI
|08:11 - 08:12|
|08:13 - 08:30|
Chu-Pan WongCarnegie Mellon University, USA, Eduard BaranovUniversité Catholique de Louvain, Belgium, Giovani GuizzoUniversity College London, UK, M. Ammar Ben KhadraTU Kaiserslautern, Germany, Porfirio TramontanaDepartment of Electrical Engineering and Information Technologies, University of Naples Federico II, Italy, Thierry Titcheu ChekamUniversity of Luxembourg (SnT), M: Marcel BöhmeMonash University, Australia