Quality Attributes Assessment in Self-Adaptive Systems: An Empirical Evaluation

Self-adaptive Systems (SAS) can monitor themselves and their context. They can detect changes and react to unexpected conditions with minimal human supervision during their execution. One of the challenges behind developing SAS is dealing with the decision-making process while analyzing the tradeoff points among the multiple quality attributes (QA). In Software Engineering, a widely accepted method of evaluating QA goals in software projects is the Architecture Tradeoff Analysis Method (ATAM). However, despite its importance and wide acceptance, there are few reports of empirical studies on analyzing QA tradeoffs in SAS. In this sense, the present investigation proposes an adapted version of ATAM called ATAM-4SAS to deal with the particularities of SAS. To achieve the research goal, we employed the UPPAAL SMC (statistical verification model) to analyze a set of QA. To evaluate the feasibility of the proposed method, we performed an empirical study on the execution of the ATAM-4SAS in a SAS developed according to the MAPE-K model. This model encompasses the Monitoring, Analysis, Planning, and Execution phases. Such steps share a knowledge base (K), which is fundamental in supporting decision-making. We complemented the empirical evaluation by conducting a focus group, which sought to assess the perceived ease of use and the perceived usefulness of the ATAM-4SAS to support the strategic choice of QA in a SAS. As a result, we observed that most participants agreed that ATAM-4SAS provides adequate support for the strategic choice of QA in SAS