Verificando a boa formação de modelos GODA

Monografia (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2016.O framework GODA (Goal-Oriented Dependability Analysis) realiza análise de dependabilidade em modelos orientados a objetivos. Uma etapa importante é o processo de geração automática de modelo DTMC (Discrete-Time Markov Chains) a partir de um modelo CRGM (Contextual and Runtime Goal Model). O modelo CRGM apresenta notações específicas não testadas. Erros neste modelo podem acarretar em problemas na geração do modelo DTMC. Como o GODA é integrado à diferentes ferramentas, a atividade de teste funcional consegue verificar a funcionalidade geral deste framework. Esse trabalho teve como objetivo o desenvolvimento de uma suíte de testes que verifique a boa formação dos modelos CRGM. Para isso, foi escolhida a abordagem de teste funcional, utilizando o critério Teste Funcional Sistemático. A partir da especificação do programa, classes de equivalência foram definidas e, em seguidas, casos de teste foram identificados. A implementação dos testes foi feita utilizando a linguagem de programação Java, e o conjunto de testes foi automatizado utilizando a ferramenta JUnit. Os resultados mostraram falhas na validação de anotações utilizadas no modelo CRGM. O desenvolvimento da suíte de testes proposta foi importante para expor problemas que podem acarretar numa geração de modelos DTMC incorretos, devido a erros no CRGM.GODA (Goal-Oriented Dependability Analysis) framework performs dependability analysis on goal models. An important step is the CRGM (Contextual and Runtime Goal Model) to DTMC (Discrete-Time Markov Chains) automated code generation. CRGM has untested notations. Errors in this model could result in problems during the DTMC model generation. Since GODA integrates many different tools, functional testing activity can control the overall functionality of this framework. The aim of this work was the development of a test suit that verifies well formedness of the CRGM model. The functional testing approach was chosen, using the Systematic Functional Testing criterion. From de software specification, equivalence classes were defined and then test cases were identified. The tests were implemented in Java, and automated using JUnit. The results showed validation failures of CRGM notes. The development of the test suit proposed was important to expose problems that can lead to incorrect DTMC models due to errors in CRGM

Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach

Goals are first-class entities in a self-adaptive system (SAS) as they guide the self-adaptation. A SAS often operates in dynamic and partially unknown environments, which cause uncertainty that the SAS has to address to achieve its goals. Moreover, besides the environment, other classes of uncertainty have been identified. However, these various classes and their sources are not systematically addressed by current approaches throughout the life cycle of the SAS. In general, uncertainty typically makes the assurance provision of SAS goals exclusively at design time not viable. This calls for an assurance process that spans the whole life cycle of the SAS. In this work, we propose a goal-oriented assurance process that supports taming different sources (within different classes) of uncertainty from defining the goals at design time to performing self-adaptation at runtime. Based on a goal model augmented with uncertainty annotations, we automatically generate parametric symbolic formulae with parameterized uncertainties at design time using symbolic model checking. These formulae and the goal model guide the synthesis of adaptation policies by engineers. At runtime, the generated formulae are evaluated to resolve the uncertainty and to steer the self-adaptation using the policies. In this paper, we focus on reliability and cost properties, for which we evaluate our approach on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the validation are promising and show that our approach is able to systematically tame multiple classes of uncertainty, and that it is effective and efficient in providing assurances for the goals of self-adaptive systems