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A Review of Random Test Case Generation using Genetic Algorithm
Background/Objectives: This research paper presents how Genetic algorithm is efficiently used in random test case generation during functional software testing. Methods/Statistical Analysis: Different hybridized Genetic Algorithms are used to generate test data automatically and optimized those test cases to solve many complex problem related to software testing. Findings: Genetic Algorithms are successfully used in software testing with increasing number of test case generation and provides a means of an automatic test case generator. Applications/Improvements: This study gives us a brief idea to implement Genetic Algorithms in software testing for optimum results and also it can be used with the neural networks and fuzzy systems for different types of testing to improve the performance.
Black-Box Testing, Fitness Function, Genetic Algorithm (GA), Neural Network, Software Testing, Test case Generation
- Jones C, Bonsignour O. The economics of software quality.Addison-Wesley Professional. 2011 July; 19.
- Acharya S, Pandya V. Bridge between Black Box and White Box–Gray Box Testing Technique. International Journal of Electronics and Computer Science Engineering. 2012; 2(1):175–85.
- Chauhan N. Software Testing: Principles and Practices, Oxford University Press. 2010.
- Jogersen PC. Software testing: A craftsman approach. 3rd edition, CRC presses. 2008.
- Srivastava PR, Ramachandran V, Kumar M, Talukder G, Tiwari V, Sharma P. Generation of test data using meta heuristic approach. TENCON 2008-2008 IEEE Region 10 Conference. 2008 November; 19: 1–6.
- Michael CC, McGraw GE, Schatz MA, Walton CC. Genetic algorithms for dynamic test data generation. Proceedings of 12th IEEE International Conference IEEE on Automated Software Engineering, 1997. 1997 November 1, p. 307-08.Crossref
- Doungsa-ard C, Dahal K, Hossain A, Suwannasart T. Test data generation from UML state machine diagrams using gas. International Conference on Software Engineering Advances (ICSEA 2007). 2007 August 25; p. 47–47. Crossref
- Srivastava PR, Kim TH. Application of genetic algorithm in software testing. International Journal of software Engineering and its Applications. 2009 October; 3(4):87–96.
- Berndt DJ, Watkins A. High volume software testing using genetic algorithms. Proceedings of the 38th Annual Hawaii International Conference on System Sciences IEEE. 2005 January 3; p. 318b. Crossref
- Dixit S, Tomar P. Automated test data generation using computational intelligence. 2015: 4th International Conference on IEEE Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions). 2015 September 2; p. 1–4. Crossref
- Sharma A, Patani R, Aggarwal A. Software testing using genetic algorithms.
- Ahmed MA, Ali F. Multiple-path testing for cross site scripting using genetic algorithms. Journal of Systems Architecture.
- March 31; 64:50–62. Crossref
- Yang S, Man T, Xu J, Zeng F, Li K. RGA: A lightweight and effective regeneration genetic algorithm for coverage-oriented software test data generation. Information and Software Technology. 2016 August 31; 76: 19–30. Crossref
- Last M, Eyal S. A fuzzy-based lifetime extension of genetic algorithms. Fuzzy sets and systems. 2005 January 1; 149(1): 131–47. Crossref
- Peng X, Lu L. A new approach for session-based test case generation by GA. 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN), IEEE. 2011 May 27; p. 91–96. Crossref
- Pinto GH, Vergilio SR. A multi-objective genetic algorithm to test data generation. 2010 22nd IEEE International Conference on Tools with Artificial Intelligence IEEE. 2010 October 27; 1: 129–34. Crossref
- Ribeiro JC, Zenha-Rela MA, de Vega FF. Test case evaluation and input domain reduction strategies for the evolutionary testing of object-oriented software. Information and Software Technology. 2009 November 30; 51(11): 1534–48. Crossref
- Goldberg DE. Genetic algorithms. Pearson Education India.2006.
- Wappler S, Lammermann F. Using evolutionary algorithms for the unit testing of object-oriented software. Proceedings of the 7th annual conference on Genetic and evolutionary computation ACM. 2005 June 25; p. 1053–60. Crossref
- Emanuelle F, Menezes R, Braga M. Using Genetic algorithms for test plans for functional testing. 44th ACM SE proceeding. 2006; p. 140–5.
- Mathur AP. Foundations of Software Testing, 2/e. Pearson Education India; 2008.
- Rauf A, Anwar S, Jaffer MA, Shahid AA. Automated GUI test coverage analysis using GA. 2010 Seventh International Conference on IEEE Information Technology: New Generations (ITNG). 2010 April 12; p. 1057–62. Crossref
- Andalib A, Babamir SM. A new approach for test case generation by discrete particle swarm optimization algorithm. 2014 22nd Iranian Conference on Electrical Engineering (ICEE) IEEE. 2014 May 20; p. 1180–85. Crossref
- Zhao R, Lv S. Neural-network based test cases generation using genetic algorithm. 13th Pacific Rim International Symposium on IEEE Dependable Computing, 2007. PRDC 2007. 2007 December 17; p. 97–100. Crossref
- Li K, Zhang Z, Kou J. Breeding software test data with geneticparticle swarm mixed algorithm. Journal of computers. 2010 January 2; 5(2): 258–65. Crossref
- Shahbazi A, Miller J. Black-Box String Test Case Generation through a Multi-Objective Optimization. IEEE Transactions on Software Engineering. 2016 April 1; 42(4): 361–78. Crossref
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