Systematic design of adaptive systems: control-based framework

• Ano de defesa: 2015
• Autor(a) principal: PIMENTEL, João Henrique Correia
• Orientador(a): CASTRO, Jaelson Freire Brelaz de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Pernambuco
• Programa de Pós-Graduação: Programa de Pos Graduacao em Ciencia da Computacao
• País: Brasil
• Palavras-chave em Português: Adaptação de Software; Engenharia de Requisitos; Projeto Arquitetural; Engenharia Dirigida a Modelos; Sistemas Adaptativos; Transformação de Modelos; Ferramenta de modelagem; Software Adaptation; Requirements Engineering; Architectural Design; Model- Driven Engineering; Adaptive Systems; Model Transformation; Modeling Tool
• Link de acesso: https://repositorio.ufpe.br/handle/123456789/15889

A number of approaches have been proposed for eliciting, modeling and analyzing requirements for adaptive systems. However, there is still a large gap between such requirements specifications and the actual implementation of adaptive systems. In this thesis we investigate the interplay between requirements and architecture for the development of adaptive systems. Furthermore, we propose the Multi-Level Adaptation for Software Systems (MULAS) framework. This framework is centered on the iterative and incremental refinement of a goal model, towards the creation of a Design Goal Model. This model can then be used at runtime to drive adaptation on a system that is properly instrumented. Moreover, the framework includes a toolsupported process for generating statechart behavioral models from a Design Goal Model. The GATO tool (Goal TO Architecture) allows the creation of the different artifacts of the process, including the automatic generation of base statecharts. The suitability of this approach for developing adaptive systems is illustrated by means of case studies. Empirical results show that the techniques developed to translate enriched goal models onto statecharts are scalable, i.e. they present a good performance even with large models. Furthermore, an experiment with software engineering students indicates that the adoption of this framework by non-experts is feasible and beneficial.