Constraint-Based Automatic Test Data Generation
IEEE Transactions on Software Engineering
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An introduction to genetic algorithms
An introduction to genetic algorithms
Prioritizing test cases for regression testing
Proceedings of the 2000 ACM SIGSOFT international symposium on Software testing and analysis
Black-box test reduction using input-output analysis
Proceedings of the 2000 ACM SIGSOFT international symposium on Software testing and analysis
Software Engineering: Theory and Practice
Software Engineering: Theory and Practice
Software Testing: A Craftman's Approach
Software Testing: A Craftman's Approach
Software Testing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Using Genetic Algorithms with Small Populations
Proceedings of the 5th International Conference on Genetic Algorithms
A fuzzy-based lifetime extension of genetic algorithms
Fuzzy Sets and Systems
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Optimisation of software testing using Genetic Algorithm
International Journal of Artificial Intelligence and Soft Computing
Quality improvement and optimization of test cases: a hybrid genetic algorithm based approach
ACM SIGSOFT Software Engineering Notes
Proceedings of the 2nd international workshop on Evidential assessment of software technologies
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Black-box (functional) test cases are identified from functional requirements of the tested system, which is viewed as a mathematical function mapping its inputs onto its outputs. While the number of possible black-box tests for any non-trivial program is extremely large, the testers can run only a limited number of test cases under their resource limitations. An effective set of test cases is the one that has a high probability of detecting faults presenting ina computer program.In this paper, we introduce a new, computationally intelligent approach to automated generation of effective test cases based on a novel, Fuzzy-Based Age Extension of Genetic Algorithms (FAexGA). The basic idea is to eliminate "bad" test cases that are unlikely to expose any error, while increasing the number of "good" test cases that have a high probability of producing an erroneous output. The promising performance of the FAexGA-based approach is demonstrated on testing a complex Boolean expression.