Dealing with Quality Attributes Assessment in Self-Adaptive System using adapted ATAM: An Empirical Evaluation

Self-adaptive systems are capable to monitor themselves and the context surrounding them, detect changes, and decide how to react to unexpected conditions with minimal human supervision at runtime. One of the challenges behind the development of self-adaptive systems is to handle the decision-making process during the analysis of tradeoff points between multiple quality attributes (QA). In Software Engineering, a widely-accepted method for evaluating software architectures QA goals is the Architecture Tradeoff Analysis Method (ATAM). However, there is a lack of studies addressing QA tradeoff analysis in self-adaptive systems development, despite its importance. In this paper, we evaluated an adaptation of the ATAM for self-adaptive systems development. We carried out an empirical evaluation within a simulated smart home environment, implemented based on the Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K) theory. The results indicated that the adaptation of ATAM is a suitable method to be used in a self-adaptive system. It supports the design decision in quality attributes concerns in self-adaptive systems that use MAPE-K model