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Search-based mutation testing for Simulink models

Published: 25 June 2005 Publication History

Abstract

The efficient and effective generation of test-data from high-level models is of crucial importance in advanced modern software engineering. Empirical studies have shown that mutation testing is highly effective. This paper describes how search-based automatic test-data generation methods can be used to find mutation adequate test-sets for Matlab/Simulink models.

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  • (2024)Timed Model-Based Mutation Operators for Simulink Models2024 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)10.1109/ICSTW60967.2024.00055(263-272)Online publication date: 27-May-2024
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  • (2023)What Not to Test (For Cyber-Physical Systems)IEEE Transactions on Software Engineering10.1109/TSE.2023.327230949:7(3811-3826)Online publication date: Jul-2023
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cover image ACM Conferences
GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation
June 2005
2272 pages
ISBN:1595930108
DOI:10.1145/1068009
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 June 2005

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Author Tags

  1. Matlab/Simulink
  2. automation
  3. heuristic search
  4. mutation testing
  5. simulated annealing
  6. test-data generation

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Cited By

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  • (2024)Timed Model-Based Mutation Operators for Simulink Models2024 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)10.1109/ICSTW60967.2024.00055(263-272)Online publication date: 27-May-2024
  • (2023)An Integrated Mutation Analysis Tool (Imat) For Simulink Models and Their Ideal ImplementationJournal of Aerospace Sciences and Technologies10.61653/joast.v69i3.2017.278(361-372)Online publication date: 31-Jul-2023
  • (2023)What Not to Test (For Cyber-Physical Systems)IEEE Transactions on Software Engineering10.1109/TSE.2023.327230949:7(3811-3826)Online publication date: Jul-2023
  • (2023)Property-Based Mutation Testing2023 IEEE Conference on Software Testing, Verification and Validation (ICST)10.1109/ICST57152.2023.00029(222-233)Online publication date: Apr-2023
  • (2022)How higher order mutant testing performs for deep learning models: A fine-grained evaluation of test effectiveness and efficiency improved from second-order mutant-classification tuplesInformation and Software Technology10.1016/j.infsof.2022.106954150(106954)Online publication date: Oct-2022
  • (2021)Metamorphic testing of autonomous vehiclesProceedings of the 43rd International Conference on Software Engineering: Companion Proceedings10.1109/ICSE-Companion52605.2021.00048(105-107)Online publication date: 25-May-2021
  • (2020)Model-Based Test Case Prioritization Using an Alternating Variable Method for Regression Testing of a UML-Based ModelApplied Sciences10.3390/app1021753710:21(7537)Online publication date: 26-Oct-2020
  • (2020)A Glance on Performance of Fitness Functions Toward Evolutionary Algorithms in Mutation TestingData Science: From Research to Application10.1007/978-3-030-37309-2_6(59-75)Online publication date: 29-Jan-2020
  • (2019)A Pipeline Computing Method of SpTV for Three-Order Tensors on CPU and GPUACM Transactions on Knowledge Discovery from Data10.1145/336357513:6(1-27)Online publication date: 11-Nov-2019
  • (2019)Attention Models in GraphsACM Transactions on Knowledge Discovery from Data10.1145/336357413:6(1-25)Online publication date: 11-Nov-2019
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