Test Case Selection Using CBIR and Clustering Research-in-Progress

Autor(es)

Narciso, Everton Note ; Delamaro, Marcio Eduardo ; dos Santos Nunes, Fatima de Lourdes ; Assoc Informat Syst

• Tipo de documento: Artigo Científico
• Fonte: AMCIS 2013 PROCEEDINGS; v. N/A, p. 9-pg., 2013-01-01.

Resumo

Choosing test cases for the optimization process of information systems testing is crucial, because it helps to eliminate unnecessary and redundant testing data. However, its use in systems that address complex domains (e.g. images) is still underexplored. This paper presents a new approach that uses Content-Based Image Retrieval (CBIR), similarity functions and clustering techniques to select test cases from an image-based test suite. Two experiments performed on an image processing system show that our approach, when compared with random tests, can significantly enhance the performance of tests execution by reducing the test cases required to find a fault. The results also show the potential use of CBIR for information abstraction, as well as the effectiveness of similarity functions and clustering for test case selection. (AU)

• Processo FAPESP: 10/15691-0 - Proposição, implementação e validação de técnicas para treinamento médico virtual interativo
• Beneficiário: Fátima de Lourdes dos Santos Nunes Marques
• Modalidade de apoio: Auxílio à Pesquisa - Regular