Software Test Data Generation Using Genetic Algorithms

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Zamen Milani, Farzad | 2009

918 Viewed

Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 39662 (02)
University: Sharif University of Technology
Department: Mathematical Sciences
Advisor(s): Mahdavi Amiri, Nezameddin
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 meant to solve difficult problems involving the simultaneous satisfaction of many constraints. Here we use human based genetic algorithm (HBGA) for its better efficiency.
Keywords:
Genetic Algorithm ; Genetic Algorithm Optimization ; Software Testing ; Automated Test Data Generation ; Function Optimization ; Human Based Genetic Algorithms

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