Managing Trade-offs in Self-Adaptive Software Architectures: A Systematic Mapping Study

Context: Self-adaptation has been driven by the need to achieve and maintain quality attributes in the face of the continuously changing requirements, as well as the uncertain demand during run-time. Designing architectures that exhibit a good trade-off between multiple quality attributes is challenging, especially in the case of self-adaptive software systems, due to the complexity, heterogeneity and ultra-large scale of modern software systems. This challenge increases with the dynamic, open and uncertain operating environment, as well as the need for complying to environmental, regulatory and sustainability requirements; such as energy consumption regulations.
Objective: This study aims at analysing the research landscape that have explicitly addressed tradeoffs management for self-adaptive software architectures, to obtain a comprehensive overview on the current state of research on this specialised area.
Method: A Systematic Mapping Study was conducted to identify and analyse research works related to analysing and managing trade-offs to support decision-making for self-adaptive software architectures.
Results: Twenty primary studies were evidently selected and analysed to classify software paradigms, quality attributes considered, and the self-* properties that drive trade-offs management. The results show constant interest in finding solutions for trade-offs management at design-time and run-time, as
well as the success of research initiatives even when new research challenges are found.
Conclusions: The findings call for foundational framework to analyse and manage trade-offs for selfadaptive software architectures that can explicitly consider specific multiple quality attributes, the run-time dynamics, the uncertainty of the environment and the complex challenges of modern, ultralarge
scale systems in particular given software paradigms.