Software Test Data Generation Using Genetic Algorithms, M.Sc. Thesis Sharif University of Technology ; Mahdavi Amiri, Nezameddin (Supervisor)
Abstract
In software testing, it is often desirable to find test inputs that exercise specific program features. Good testing means uncovering as many faults as possible with a potent set of tests. Thus, a test series that has the potential to uncover many faults is better than one that can only uncover a few. To find these inputs by hand is extremely time-consuming, especially when the software is complex. Therefore, many attempts have been made to automate the process. There are three major methods to generate software test data: Random test data generation, Symbolic test data generation and Dynamic test data generation. Dynamic test data generation, such as those using genetic algorithms, is...
Cataloging briefSoftware Test Data Generation Using Genetic Algorithms, M.Sc. Thesis Sharif University of Technology ; Mahdavi Amiri, Nezameddin (Supervisor)
Abstract
In software testing, it is often desirable to find test inputs that exercise specific program features. Good testing means uncovering as many faults as possible with a potent set of tests. Thus, a test series that has the potential to uncover many faults is better than one that can only uncover a few. To find these inputs by hand is extremely time-consuming, especially when the software is complex. Therefore, many attempts have been made to automate the process. There are three major methods to generate software test data: Random test data generation, Symbolic test data generation and Dynamic test data generation. Dynamic test data generation, such as those using genetic algorithms, is...
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