In software testing, it is often desirable to find test inputs that can handle specific features of the program. 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. Random test data generation consists of generating test inputs at random, in the hope that they will exercise the desired software features. Often, the desired inputs must satisfy complex constraints, and this makes a random approach seem unlikely to succeed. In contrast, combinatorial optimization techniques, such as those using genetic algorithms, are meant to solve difficult problems involving the simultaneous satisfaction of many constraints. This paper presents a technique that uses a genetic algorithm for automatic test-data generation. A genetic algorithm is a heuristic that mimics the evolution of natural species in searching for the optimal solution to a problem. In test data generation application, the solution sought by the genetic algorithm is test data that causes execution of a given statement, branch, path, or definition-use pair in the program under test.

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