A Novelty Search-based Test Data Generator for Object-oriented Programs
Abstract
In search-based structural testing, meta-heuristic search techniques have been frequently used to automate test data generation. In this paper, we introduce the use of novelty search algorithm to the test data generation problem based on statement-covered criterion. In this approach, we seek to explore the search space by considering diversity as the unique objective function to be optimized. In fact, instead of having a fitness-based selection, we select test cases based on a novelty score showing how different they are compared to all other solutions evaluated so far.
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