Defesa de Tese de Doutorado N.º 36 - Ana Emília Victor Barbosa Coutinho, em 20/03, às 10hs

postado em 20 de fev. de 2015 10:08 por Franklin de Souza Ramalho   [ 3 de mar. de 2015 13:20 atualizado‎(s)‎ ]
Candidato: Ana Emília Victor Barbosa Coutinho

Título do trabalho: Similarity-based Test Suite Reduction in the Context of Model-Based Testing

Patrícia Duarte de Lima Machado Emanuela Gadelha Cartaxo

Data: 20/03/2015
Horário: 10:00
Local: SPLAB

Banca examinadora: Patrícia Duarte de Lima Machado Emanuela Gadelha Cartaxo (orientadoras), Anamaria Martins Moreira (UFRJ), Juliano Iyoda (UFPE), Jorge César Abrantes de Figueiredo (UFCG), Wilkerson de Lucena Andrade (UFCG).

Software testing is an important and expensive activity of the software development process to evaluate the product quality. In order to reduce the cost for generation of test cases, Model-based Testing (MBT) approaches have been proposed. It provides the benefit of automatic test case generation from abstract models that capture, for instance, software requirements. Despite the fact that automation is critical to the practice of MBT, test case generation for industrial size applications can often produce large test suites that may not be cost-effective. Particularly, a test suite can have a large amount of possibly redundant test cases (in relation to one set of test requirements). In order to handle this problem, different studies have been developed aimed at reducing the costs related to the size of an automatically generated test suite. Test suite reduction (also known as test suite minimization) aims to produce a representative subset of the complete test suite that satisfies a set of test requirements with the same coverage as the complete test suite Most reduction strategies proposed in literature are usually based on heuristics to maximize coverage and rate of reduction. On the other hand, few reduction strategies consider the similarity degree among test cases. In this sense, in this thesis, a new parameterized reduction strategy based on the use of a similarity function and multi-criteria in the MBT context is proposed. The idea is to identify the degree of similarity among the test cases and keep in the suite the most different ones that together can meet a set of test requirements, and at the same time maintaining a little redundancy in the reduced suite with the applicability of the multiple criteria. First, we investigate the effectiveness of distance functions for our similarity-based test suite reduction strategy in the context of MBT. Results show that the choice of a distance function has little influence on the size of the reduced test suite. However, as reduced suites are different depending on the distance function applied, the choice can significantly affect fault coverage and stability. Afterwards, we compare our reduction strategy with other four well-known test suite reduction heuristics that can be applied in the MBT context by using different coverage criteria for transition-based coverage criteria. Results show that choice of the coverage criteria to reduce strategy can significantly affect reduction and fault coverage rates. Furthermore, our strategy showed promising results regarding fault coverage with bi-criteria.