A Novelty Search Approach for Automatic Test Data GenerationReportar como inadecuado

A Novelty Search Approach for Automatic Test Data Generation - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

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, 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.

Autor: Mohamed Boussaa - Olivier Barais - Gerson Sunyé - Benoit Baudry -

Fuente: https://hal.archives-ouvertes.fr/


Documentos relacionados