RE-STORM: Mapping the Decision-Making Problem and Non-functional Requirements Trade-Off to Partially Observable Markov Decision Processes

Different model-based techniques have been used to model and underpin requirements management and decision-making strategies under uncertainty for self-adaptive Systems (SASs). The models specify how the partial or total fulfillment of non-functional requirements (NFRs) drive the decision-making process at runtime. There has been considerable progress in this research area. How-ever, precarious progress has been made by the use of models at runtime using machine learning to deal with uncertainty and support decision-making based on new evidence learned during execution. New techniques are needed to systematically revise the current model and the satisficement of its NFRs when empirical evidence becomes available from the monitoring infrastructure. In this paper, we frame the decision-making problem and trade-off specifications of NFRs in terms of Partially Observable Markov Decision Processes (POMDPs) models. The mathematical probabilistic framework based on the concept of POMDPs serves as a runtime model that can be updated with new learned evidence to support reasoning about partial satisficement of NFRs and their trade-o under the new changes in the environment. In doing so, we demonstrate how our novel approach RE-STORM underpins reasoning over uncertainty and dynamic changes during the system's execution.