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Automatic mutation test input data generation via ant colony

Published: 07 July 2007 Publication History

Abstract

Fault-based testing is often advocated to overcome limitations ofother testing approaches; however it is also recognized as beingexpensive. On the other hand, evolutionary algorithms have beenproved suitable for reducing the cost of data generation in the contextof coverage based testing. In this paper, we propose a newevolutionary approach based on ant colony optimization for automatictest input data generation in the context of mutation testingto reduce the cost of such a test strategy. In our approach the antcolony optimization algorithm is enhanced by a probability densityestimation technique. We compare our proposal with otherevolutionary algorithms, e.g., Genetic Algorithm. Our preliminaryresults on JAVA testbeds show that our approach performed significantlybetter than other alternatives.

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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
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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Published: 07 July 2007

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Author Tags

  1. ant colony optimization
  2. mutation testing
  3. search based testing
  4. test input data generation

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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  • (2024)Optimization Study of Neural Network Mutation Testing Data Based on Set Base Evolutionary Algorithm2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE)10.1109/CISCE62493.2024.10653181(1186-1192)Online publication date: 10-May-2024
  • (2023)Fuzzing for CPS Mutation Testing2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)10.1109/ASE56229.2023.00079(1377-1389)Online publication date: 11-Sep-2023
  • (2022)Enhancement of Mutation Testing via Fuzzy Clustering and Multi-Population Genetic AlgorithmIEEE Transactions on Software Engineering10.1109/TSE.2021.305298748:6(2141-2156)Online publication date: 1-Jun-2022
  • (2022)Towards Agile Mutation Testing Using Branch Coverage Based Prioritization TechniqueLean and Agile Software Development10.1007/978-3-030-94238-0_9(150-169)Online publication date: 12-Jan-2022
  • (2020)Efficiently Generating Test Data to Kill Stubborn Mutants by Dynamically Reducing the Search DomainIEEE Transactions on Reliability10.1109/TR.2019.292268469:1(334-348)Online publication date: Mar-2020
  • (2020)Scaling Test Case Generation For Expressive Decision Tables2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST)10.1109/ICST46399.2020.00044(364-374)Online publication date: Oct-2020
  • (2020)An Improved Crow Search Algorithm for Test Data Generation Using Search-Based Mutation TestingNeural Processing Letters10.1007/s11063-020-10288-7Online publication date: 20-Jun-2020
  • (2020)Short-Term Electricity Load Forecast Using Hybrid Model Based on Neural Network and Evolutionary AlgorithmNumerical Optimization in Engineering and Sciences10.1007/978-981-15-3215-3_16(167-176)Online publication date: 8-Apr-2020
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  • (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
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