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A Novelty Search-based Test Data Generator for Object-oriented Programs

Mohamed Boussaa 1 Olivier Barais 1 Gerson Sunyé 2, 3 Benoit Baudry 1
1 DiverSe - Diversity-centric Software Engineering
Inria Rennes – Bretagne Atlantique , IRISA-D4 - LANGAGE ET GÉNIE LOGICIEL
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, 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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Submitted on : Thursday, December 24, 2015 - 12:14:30 PM
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Mohamed Boussaa, Olivier Barais, Gerson Sunyé, Benoit Baudry. A Novelty Search-based Test Data Generator for Object-oriented Programs. GECCO 2015, Jul 2015, Madrid, Spain. pp.1359--1360, ⟨10.1145/2739482.2764716⟩. ⟨hal-01248177⟩

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