Identifying efficient approaches to automatically generate test cases in model based testing

• Ano de defesa: 2019
• Autor(a) principal: Matheus Monteiro Mariano
• Orientador(a): Nandamudi Lankalapalli Vijaykumar, Érica Ferreira de Souza
• Banca de defesa: Celso Luiz Mendes, André Takeshi Endo, Luciana Brasil Rebelo dos Santos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Instituto Nacional de Pesquisas Espaciais (INPE)
• Programa de Pós-Graduação: Programa de Pós-Graduação do INPE em Computação Aplicada
• País: BR
• Link de acesso: http://urlib.net/sid.inpe.br/mtc-m21c/2019/04.15.15.09

Context: Model Based Testing (MBT) has attracted a lot of attention from researchers since it has proved efficient in using formal models to represent reactive systems behavior in order to guide test case generation. Such systems are mostly specified and verified using Finite State Machine (FSM), a formal modeling technique commonly used to represent systems behavior. There is a plethora of test generation algorithms in the literature. Most of them are graph-based once a FSM can be considered as a graph. Nevertheless, there is a lack of studies on analyzing cost and efficiency of FSM-based test generation algorithms. Objective: This dissertation aims to investigate and compare graph-based algorithms employed to generate test cases from FSM models. In particular, we compare the Chinese Postman Problem (CPP) and H-Switch Cover (HSC) algorithms with the well-known breadth-first and depth-first search (BFS, DFS) algorithms in the context of covering all-transitions (AT) and all-transition-pairs (ATP) criteria in a FSM. Method: First, a systematic literature mapping was conducted to summarize the methods that have been adopted in MBT, considering FSM. Second, the main methods found were implemented and analyzed on random and real-world FSMs that represent embedded systems of space applications. For the evaluation of studies, we considered analyses in terms of cost (time), efficiency (mutant analysis) and coverage of the generated test cases (number of test cases, average length of test cases, largest and smallest test cases, etc.). Results: In general, CPP presented the best results with the FSMs used in terms of number of test cases and test suite size. In addition, CPP also presented low distribution of average length compared to other algorithms.