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Optimum Selection of GA Algorithm's Parameters for Software Test Data Generation


sonam kamboj , Maharishi Vedvyas Engineering College; mohinder singh, Maharishi Vedvyas Engineering College


Black box testing, White box testing, Dynamic execution , Symbolic execution, GA search algorithms , Metaheuristic Search Algorithms.


The proposed research work implemented and fine-tuned meta-heuristic based search algorithms namely GA algorithm for automatic test case generation using path testing criterion. The three parameters namely size of population, crossover rate and mutation rate have been chosen for setting GA algorithm for test data generation. For input generation, symbolic execution method has been used in which first, target path is selected from Control Flow Graph (CFG) of Software under Test (SUT) and then inputs are generated using search algorithms which can evaluate composite predicate corresponding to the target path true. We have experimented on two real world programs showing the applicability of these techniques in genuine testing environment. The algorithm is implemented using MATLAB programming environment. The performance of the algorithms is measured using average test cases generated per path (ATCPP) and average percentage coverage (APC) metrics. Experimentations found that crossover rate and mutation rates should be 80% and 5% respectively and population size can vary from 20 to 40 for best results.

Other Details

Paper ID: IJSRDV2I5059
Published in: Volume : 2, Issue : 5
Publication Date: 01/08/2014
Page(s): 199-204

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