Mutation 2020
Sat 24 Oct 2020 Porto, Portugal
co-located with ICST 2020
Sat 24 Oct 2020 15:30 - 15:45 at Arrábida - Session II

Mutation Testing (MT) is a state-of-the-art technique for assessing test suite effectiveness. The MT principle is to inject variants, known as mutants, into the System Under Test (SUT). Then, the behaviour of the original SUT is compared to that of the mutated SUT when running the same test suite. If no difference in behaviour is observed, the mutant is said to have survived; otherwise, it is said to have been killed. Despite its strengths, the applicability of MT in practice has been limited by its high computational cost. To mitigate this problem, Predictive Mutation Testing (PMT) has been proposed. PMT uses a classification model based on features related to the mutated code and the test suite to predict the execution results of a mutant without actually executing it. In other words, PMT predicts whether a mutant will be killed or will survive. In previous studies, PMT has been evaluated on several projects in two application scenarios, involving cross-project and crossversion learning. The goal of our research is to investigate how well the proposed PMT method, which has been evaluated on Java, can be extended to other programming languages. For that purpose, we first replicated the previous study and then extended the PMT approach to a single C program. We used random forrest classifiers as our supervised learning approach of choice. Our results indicate that PMT is able to predict the execution results of mutants with high accuracy. On the Java projects, we achieved Area Under Curve (AUC) values above 0.90 with a Prediction Error (PE) below 10%. On the C project, we achieved an AUC value above 0.90 with a PE below 1%. In our analyses we also investigated how sensitive the performance of PMT is to the set of selected features. In particular, we wanted to understand whether adding programming language specific features to a language independent core set of features significantly improve the performance of PMT. Our results are an indicator that, overall, PMT has potential to be applied across programming languages and is robust when dealing with imbalanced data.

Sat 24 Oct
Times are displayed in time zone: Greenwich Mean Time : Lisbon change

15:15 - 16:30: Session IIMutation 2020 at Arrábida
15:15 - 15:30
Full-paper
Mutation 2020
Alessandro V. PizzoletoFederal University of Sao Carlos, Fabiano FerrariFederal University of São Carlos, Lucas D. DalliloUniversity of Sao Paulo, Jeff OffuttGeorge Mason University
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15:30 - 15:45
Full-paper
Mutation 2020
Alejandra Duque TorresInstitute of Computer Science, University of Tartu, Natia DoliashviliInstitute of Computer Science, University of Tartu, Dietmar PfahlUniversity of Tartu, Rudolf RamlerSoftware Competence Center Hagenberg
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15:45 - 16:00
Full-paper
Mutation 2020
Bento Rafael SiqueiraFederal University of São Carlos, Fabiano FerrariFederal University of São Carlos, Kathiani E. SouzaFederal University of São Carlos, Daniel S. M. SantibáñezFederal University of São Carlos, Valter Vieira CamargoFederal University of Sergipe, São Cristovão
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16:00 - 16:15
Full-paper
Mutation 2020
Michael Baer, Norbert Oster, Michael PhilippsenFriedrich-Alexander University Erlangen-Nürnberg (FAU)
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16:15 - 16:30
Full-paper
Mutation 2020
Claudinei Brito JuniorUniversidade de São Paulo, Vinicius DurelliUniversidade Federal de São João del-Rei, Rafael S. DurelliFederal University of Lavras Lavras, Simone do Rocio Senger de SouzaUniversity of São Paulo - USP, Auri VincenziFederal University of São Carlos, Marcio Eduardo DelamaroUniversidade de São Paulo
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