Approaches that support the decision-making of self-adaptive and autonomous systems (SAS) often consider an idealized situation where (i) the system’s state is treated as fully observable by the monitoring infrastructure, and (ii) adaptation actions are assumed to have known, deterministic effects over the system. However, in practice, the system’s state may not be fully observable, and the adaptation actions may produce unexpected effects due to uncertain factors. This article presents a novel probabilistic approach to quantify the uncertainty associated with the effects of adaptation actions on the state of a SAS. Supported by Bayesian inference and POMDPs (Partially-Observable Markov Decision Processes), these effects are translated into the satisfaction levels of the non-functional requirements (NFRs) to, therefore, drive the decision-making. The approach has been applied to two substantial case studies from the networking and Internet of Things (IoT) domains, using two different POMDP solvers. The results show that the approach delivers statistically significant improvements in supporting decision-making for SAS.

to investigate the role of values in technology acceptance in general and in the context of the UK Covid Track and Trace App. 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. 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.

[Context/Motivation] A model at runtime can be defined as an abstract representation of a system, including its structure and behaviour, which exist alongside with the running system. Runtime models provide support for decision-making and reasoning based on design-time knowledge but, also based on information that may emerge at runtime and which was not foreseen before execution. [Questions/Problems] A challenge that persists is the update of runtime models during the execution to support up-to-date information for reasoning and decision-making. New techniques based on machine learning (ML) and Bayesian Learning offer great potential to support the update of runtime models during execution. Runtime models can be updated using these new techniques to, therefore, offer better-informed decision-making based on evidence collected at runtime. The techniques we use in this paper are based on a novel implementation of Partially Observable Markov Decision Processes (POMDPs). [Contribution] In this paper, we demonstrate how given the requirements specification, a Requirements-aware runtime model based on POMDPs (RaM-POMDP) is defined. We study in detail the nature of such runtime models coupled with consideration of the Bayesian inference algorithms and tools that provide evidence of unexpected/surprising changes in the environment. We show how the RaM-POMDPs and the MAPE-K loop offer the basis of the software architecture presented and how the required casual connection of runtime models is realized. Specifically, we demonstrate how according to evidence of changes in the systems, collected by the monitoring infrastructure and using Bayesian inference, the runtime models are updated and inferred (i.e. the first aspect of the causal connection). We also demonstrate how the running system changes its runtime model, producing therefore the corresponding self-adaptations. These self-adaptations are reflected on the managed system (i.e. the second aspect of the causal connection) to better satisfice the requirements specifications and improve conformance to its service level agreements (SLAs). The experiments have been applied to a real case study for the networking application domain.

Models are not static entities: they evolve over time due to changes. Changes may inadvertently and surprisingly violate constraints imposed. Therefore, the models need to be monitored for compliance. On the one hand, in traditional design-time applications, new and evolving requirements impose changes on a model over time. These changes may accidentally break design rules. Further, the growing complexity of the models may need to be tracked for manageability. On the other hand, newer applications use models at runtime; building runtime abstractions that are used to control a system. Adopters of these approaches will need to query the history of the system to check if the models evolved as expected, or to find out the reasons for a particular behavior. Changes over models at runtime are more frequent than changes over design models. To cover these demands, we argue that a flexible and scalable approach for querying the history of the models is needed to study the evolution and for compliance sake. This paper presents a set of extensions to a model query language inspired in the Object Constraint Language (the Epsilon Object Language) for traversing the history of a model, and for making temporal assertions that will allow the elicitation of historic information. As querying long histories may be costly, the paper presents an approach that annotates versions of interest as they are observed, in order to provide efficient recalls in possible future queries. The approach has been implemented in a model indexing tool, and is demonstrated through a case study from the autonomous and self-adaptive systems domain.

Prioritization of non-functional requirements (NFRs) is a research field that needs more attention. We demonstrate ARRoW, a novel approach for automatic runtime reappraisal and update of the weights of NFRs given new evidence collected from the environment during the execution of the system. In this paper, we showcase how ARRoW is used in an substantial industrial case study. Our results shows how the approach offers a better-informed decision-making process by allowing the reappraisal and update of the weights of the NFRs in accordance to the newly detected environmental contexts.

[Context/Motivation] Decision-making for self-adaptive systems (SAS) requires the runtime trade-off of multiple non-functional requirements (NFRs) and the costs-benefits analysis of the alternative solutions. Usually, it requires the specification of weights for NFRs and decision-making strategies. Generally, these weights are defined at design-time with the support of previous experiences and domain experts. [Questions/Problems] Under some specific conditions detected at runtime, it can be the case that the weights assigned to the NFR at design time may not be suitable anymore at runtime. As a result, the system may not behave in the expected way and it may either execute unnecessary adaptations or miss crucial adaptations with a detrimental effect on the behaviour of the system. [New ideas/ early results] In this RE@Next! paper, we introduce a novel approach for automatic runtime reappraisal of the weights of NFRs given new evidence collected from the environment during the execution of the system. Our early results suggest, as expected, that the approach improves the decision-making process by allowing the reappraisal and update of the weights of the NFRs in accordance to the newly detected environmental context.

Modelling and reasoning with prioritization of non-functional requirements (NFRs) is a research field that needs more attention. We demonstrate RE-PREF, an approach that supports the modelling of NFRs and their preferences, and discovery of possible scenarios where badly chosen preferences can either make the runtime system miss or suggest unnecessary adaptations that may degrade the behavior of a self-adaptive system (SAS). Specifically, we showcase how RE-PREF is used in a remote data mirroring (RDM) system. The model of NFRs and the analysis of their preferences are enabled by using dynamic decision network (DDNs) and Bayesian Surprise.

Very small entities, enterprises, organizations, projects or departments with up to 25 people, are very important to the worldwide economy. However it has ben established that such entities often do not utilize existing standards and frameworks. To address the needs of Very Small Entities (VSEs), a set of \ninternational standards and guides known as ISO/IEC 29110 has been developed. In this paper we present the results of early trials of this standard in two IT start-ups VSEs. A Peruvian VSE was recently audited and issued an ISO/IEC 29110 certificate of conformity.