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 fulfilment of non-functional requirements (NFRs) drive the decision-making process at runtime. There has been considerable progress in this research area. However, 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-off 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.
|Title of host publication||Proceedings of the 13th International Conference on Software Engineering for Adaptive and Self-Managing Systems, SEAMS@ICSE 2018, Gothenburg, Sweden, May 28-29, 2018|
|Number of pages||7|
|Publication status||Published - 28 May 2018|
|Event||13th International Conference on Software Engineering for Adaptive and Self-Managing Systems - Gothenburg, Sweden|
Duration: 28 May 2018 → 29 May 2018
|Conference||13th International Conference on Software Engineering for Adaptive and Self-Managing Systems|
|Period||28/05/18 → 29/05/18|