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Fri 13 Nov 2020 08:11 - 08:12 at Virtual room 1 - Testing 3

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).

Fri 13 Nov

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08:00 - 08:30
08:00
2m
Talk
Baital: An Adaptive Weighted Sampling Approach for Improved t-wise Coverage
Research Papers
Eduard Baranov Université Catholique de Louvain, Belgium, Axel Legay Université Catholique de Louvain, Belgium, Kuldeep S. Meel National University of Singapore, Singapore
DOI
08:03
1m
Research paper
Cost Measures Matter for Mutation Testing Study Validity
Research Papers
Giovani Guizzo University College London, UK, Federica Sarro University College London, UK, Mark Harman University College London, UK
DOI Pre-print
08:05
1m
Talk
Developing and Evaluating Objective Termination Criteria for Random Testing
Journal First
Porfirio Tramontana Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Italy, Domenico Amalfitano University of Naples Federico II, Nicola Amatucci Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Italy, Atif Memon Apple Inc., Anna Rita Fasolino Federico II University of Naples
08:07
1m
Talk
Efficient Binary-Level Coverage Analysis
Research Papers
M. Ammar Ben Khadra TU Kaiserslautern, Germany, Dominik Stoffel TU Kaiserslautern, Germany, Wolfgang Kunz TU Kaiserslautern, Germany
DOI Pre-print Media Attached
08:09
1m
Talk
Efficiently Finding Higher-Order Mutants
Research Papers
Chu-Pan Wong Carnegie Mellon University, USA, Jens Meinicke Carnegie Mellon University, USA, Leo Chen Carnegie Mellon University, USA, João Paulo Diniz Federal University of Minas Gerais, Brazil, Christian Kästner Carnegie Mellon University, USA, Eduardo Figueiredo Federal University of Minas Gerais, Brazil
DOI
08:11
1m
Talk
Selecting Fault Revealing Mutants
Journal First
Thierry Titcheu Chekam University of Luxembourg (SnT), Mike Papadakis University of Luxembourg, Luxembourg, Tegawendé F. Bissyandé University of Luxembourg, Luxembourg, Yves Le Traon University of Luxembourg, Luxembourg, Koushik Sen University of California at Berkeley
08:13
17m
Talk
Conversations on Testing 3
Paper Presentations
Chu-Pan Wong Carnegie Mellon University, USA, Eduard Baranov Université Catholique de Louvain, Belgium, Giovani Guizzo University College London, UK, M. Ammar Ben Khadra TU Kaiserslautern, Germany, Porfirio Tramontana Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Italy, Thierry Titcheu Chekam University of Luxembourg (SnT), M: Marcel Böhme Monash University, Australia