Effective Test Data Generation using Genetic Algorithms
نویسنده
چکیده
Software testing is an indispensible part of software development. It increases the confidence of programmer and user in the reliability and accuracy of software. However, it is a laborious and time-consuming task. Almost half of the software development resources spend on testing the software. Automatic software testing can substantially reduce the cost of development of software. Further exhaustive software testing is not feasible. Only the selective parts of the software are tested. Therefore design of a set of test cases is required in such a manner that it can find out as many faults as possible. We propose to improve software-testing efficiency with suitable optimization techniques. In this paper, the focus is on the use of genetic algorithms for generating the test data that can cover the most error-prone path; so that emphasis can be given on testing these paths firstly. Genetic algorithms are iterative techniques that apply simple operations repeatedly in the search for good solutions, or in this case, test data. By finding out the most error-prone path using this technique will help to reduce the software development cost and improve the testing efficiency.
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