Automatic Test Data Generation for Data Flow Testing Using a Genetic Algorithm
Moheb R. Girgis (Department of Computer Science, Faculty of Science Minia University, Egypt)
Abstract: One of the major difficulties in software testing is the automatic generation of test data that satisfy a given adequacy criterion. This paper presents an automatic test data generation technique that uses a genetic algorithm (GA), which is guided by the data flow dependencies in the program, to search for test data to cover its def-use associations. The GA conducts its search by constructing new test data from previously generated test data that are evaluated as effective test data. The approach can be used in test data generation for programs with/without loops and procedures. The proposed GA accepts as input an instrumented version of the program to be tested, the list of def-use associations to be covered, the number of input variables, and the domain and precision of each input variable. The algorithm produces a set of test cases, the set of def-use associations covered by each test case, and a list of uncovered def-use associations, if any. In the parent selection process, the GA uses one of two methods: the roulette wheel method or a proposed method, called the random selection method, according to the user choice. Finally, the paper presents the results of the experiments that have been carried out to evaluate the effectiveness of the proposed GA compared to the random testing technique, and to compare the proposed random selection method to the roulette wheel method.
Keywords: Genetic algorithms, automatic test data generation, data flow testing, software testing
Categories: D.2.5, K.6.3