A Novelty Search Approach for Automatic Test Data Generation

Mohamed Boussaa, Olivier Barais, Gerson Sunye, Benoit Baudry
2015 2015 IEEE/ACM 8th International Workshop on Search-Based Software Testing  
In search-based structural testing, metaheuristic search techniques have been frequently used to automate the test data generation. In Genetic Algorithms (GAs) for example, test data are rewarded on the basis of an objective function that represents generally the number of statements or branches covered. However, owing to the wide diversity of possible test data values, it is hard to find the set of test data that can satisfy a specific coverage criterion. In this paper, we introduce the use of
more » ... Novelty Search (NS) algorithm to the test data generation problem based on statement-covered criteria. We believe that such approach to test data generation is attractive because it allows the exploration of the huge space of test data within the input domain. In this approach, we seek to explore the search space without regard to any objectives. 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.
doi:10.1109/sbst.2015.17 dblp:conf/icse/BoussaaBSB15 fatcat:bzl6yf33gbhlbiflz5wb25qwbi