Abstract
The automatic generation of test cases oriented paths in an effective manner is a challenging problem for structural testing of software. The use of search-based optimization methods, such as genetic algorithms (GAs), has been proposed to handle this problem. This paper proposes an improved adaptive genetic algorithm (IAGA) for test cases generation by maintaining population diversity. It uses adaptive crossover rate and mutation rate in dynamic adjustment according to the differences between individual similarity and fitness values, which enhances the exploitation of searching global optimum. This novel approach is experimented and tested on a benchmark and six industrial programs. The experimental results confirm that the proposed method is efficient in generating test cases for path coverage.
Highlights
Automatic software testing is among the most studied topics in the field of search-based software engineering (SBSE) [1,2,3]
Test cases generation based on improved adaptive genetic algorithm (IAGA)
This paper proposed an automatic test case generation method based on an improved genetic algorithm
Summary
Automatic software testing is among the most studied topics in the field of search-based software engineering (SBSE) [1,2,3]. In the generation of test cases using heuristic search, feedback information concerning the tested application is used to determine whether the test data meet the testing requirements. The feedback mechanism gradually adjusts test data input until test requirements are met. This kind of method was proposed in [4], and has led to a considerable amount of subsequent research [5,6,7,8]. Fu B proposed a kind of software test data automated generation method based on simulated annealing genetic algorithm [27].An optimized technique for test case generation using tabu search and data clustering was proposed in [28]
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