Requirements-aware models to support better informed decision-making for self-adaptation using partially observable Markov decision processes

A self-adaptive system (SAS) is a system that can adapt its behaviour in re- sponse to environmental fluctuations at runtime and its own changes. Therefore, the decision-making process of a SAS is challenged by the underlying uncertainty. In this dissertation, the author focuses on the kind of uncertainty associated with the satisficement levels of non-functional requirements (NFRs) given a set of design decisions reflected on a SAS configuration. Specifically, the focus of this work is on the specification and runtime handling of the uncertainty related to the levels of satisficement of the NFRs when new evidence is collected, and that may create the need of adaptation based on the reconfiguration of the system. Specifically, this dissertation presents two approaches that address decision-making in SASs in the face of uncertainty. First, we present RE-STORM, an approach to support decision- making under uncertainty, which uses the current satisficement level of the NFRs in a SAS and the required trade-offs, to therefore guide its self-adaptation. Second, we describe ARRoW, an approach for the automatic reassessment and update of initial preferences in a SAS based on the current satisficement levels of its NFRs. We eval- uate our proposals using a case study, a Remote Data Mirroring (RDM) network. Other cases have been used as well in different publications. The results show that under uncertain environments, which may have not been foreseen in advance, it is feasible that: (a) a SAS reassess the preferences assigned to certain configurations and, (b) reconfigure itself at runtime in response to adverse conditions, in order to keep satisficing its requirements

Runtime models based on dynamic decision networks:enhancing the decision-making in the domain of ambient assisted living applications

Dynamic decision-making for self-Adaptive systems (SAS) requires the runtime trade-off of multiple non-functional requirements (NFRs) -Aka quality properties-And the costsbenefits analysis of the alternative solutions. Usually, it requires the specification of utility preferences for NFRs and decisionmaking strategies. Traditionally, these preferences have been defined at design-Time. In this paper we develop further our ideas on re-Assessment of NFRs preferences given new evidence found at runtime and using dynamic decision networks (DDNs) as the runtime abstractions. Our approach use conditional probabilities provided by DDNs, the concepts of Bayesian surprise and Primitive Cognitive Network Process (P-CNP), for the determination of the initial preferences. Specifically, we present a case study in the domain problem of ambient assisted living (AAL). Based on the collection of runtime evidence, our approach allows the identification of unknown situations at the design stage

To download or not to download the Covid-19 Track and Trace App? What is more influential in users’ minds?

Objectives: to investigate the role of values in technology acceptance in general and in the context of the UK Covid Track and Trace App. Methods: A survey and interview study was conducted to elicit users’ perceptions of values in general, values in relation to choice of IT products and values which were influenced the decision to download (or not) the NHS Covid-19 Track and Trace App. Other non-value issues such as utility, price and recommendations were considered. Results: Users’ value in life differ slightly from those considered important for selecting IT products. For general IT product decisions, functionality, trust and price with values equality, security and sustainability were important. For the Covid-19 App decision two values, helpfulness and equality, with recommendations/trust and operating system compatibility, were the main influences. Interview data indicated that downloader users were motivated by social responsibility and utility – being able to access workplaces and leisure venues – while non-downloaders had little perceived need for the App, combined with mistrust of the App's provenance (NHS and the Government) linked to security and privacy concerns. The implications for values in technology acceptance decisions are discussed