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A Novelty Search Approach for Automatic Test Data Generation

Mohamed Boussaa 1 Olivier Barais 1 Gerson Sunyé 2 Benoit Baudry 1
1 DiverSe - Diversity-centric Software Engineering
IRISA-D4 - LANGAGE ET GÉNIE LOGICIEL, Inria Rennes – Bretagne Atlantique
2 ATLANMOD - Modeling Technologies for Software Production, Operation, and Evolution
LINA - Laboratoire d'Informatique de Nantes Atlantique, Département informatique - EMN, Inria Rennes – Bretagne Atlantique
Abstract : 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 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.
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Submitted on : Friday, February 27, 2015 - 6:55:33 PM
Last modification on : Thursday, January 16, 2020 - 1:13:49 AM
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Mohamed Boussaa, Olivier Barais, Gerson Sunyé, Benoit Baudry. A Novelty Search Approach for Automatic Test Data Generation. 8th International Workshop on Search-Based Software Testing SBST@ICSE 2015, May 2015, Firenze, Italy. pp.4. ⟨hal-01121228⟩

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