Dealing with Belief Uncertainty in Domain Models

Burgueño, Loli; Muñoz, Paula; Clarisó, Robert; Cabot, Jordi; Gérard, Sébastien; Vallecillo, Antonio

doi:10.1145/3542947

Cited by 17 publications

(5 citation statements)

References 52 publications

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“…Uncertainty caused by the size of the variability space that the adaption functions need to handle. This type of uncertainty arises from striving to capture the whole complex relationship of the system with its changing environment in a few architectural configurations which is inherently difficult and generates the risk of overlooking important environmental states [5].…”

Section: Variability Space Of Adaptationmentioning

confidence: 99%

“…How to combine two types of uncertainty, namely measurement uncertainty and belief uncertainty, is not easy. In this case, Subjective logic [24] could be used to represent opinions, combine them with the measurement values, and thereby help the two engineers reach a consensus using a fusion operator [5].…”

Section: Uncertainties Due To Goal and Adaptation Functionsmentioning

confidence: 99%

“…Examples of such situations are a facial recognition system that misidentifies injured people in a search and rescue operation due to a natural disaster, an automatic triage application that misclassifies an emergency room patient as low risk when she is actually critically ill, or the automatic stock management system that decides to make a substantial investment based solely on the confidence of a computer system prediction, but without considering its associated uncertainty, can all lead to disastrous consequences if the wrong decision is made. Having a measure of the degree of uncertainty associated with a resulting decision can be useful, for example to rule out any decision whose degree of uncertainty is above a certain threshold [5,24].…”

Section: Introductionmentioning

confidence: 99%

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The uncertainty interaction problem in self-adaptive systems

et al. 2022

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The problem of mitigating uncertainty in self-adaptation has driven much of the research proposed in the area of software engineering for self-adaptive systems in the last decade. Although many solutions have already been proposed, most of them tend to tackle specific types, sources, and dimensions of uncertainty (e.g., in goals, resources, adaptation functions) in isolation. A special concern are the aspects associated with uncertainty modeling in an integrated fashion. Different uncertainties are rarely independent and often compound, affecting the satisfaction of goals and other system properties in subtle and often unpredictable ways. Hence, there is still limited understanding about the specific ways in which uncertainties from various sources interact and ultimately affect the properties of self-adaptive, software-intensive systems. In this SoSym expert voice, we introduce the Uncertainty Interaction Problem as a way to better qualify the scope of the challenges with respect to representing different types of uncertainty while capturing their interaction in models employed to reason about self-adaptation. We contribute a characterization of the problem and discuss its relevance in the context of case studies taken from two representative application domains. We posit that the Uncertainty Interaction Problem should drive future research in software engineering for autonomous and self-adaptive systems, and therefore, contribute to evolving uncertainty modeling towards holistic approaches that would enable the construction of more resilient self-adaptive systems.

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Section: Variability Space Of Adaptationmentioning

confidence: 99%

Section: Uncertainties Due To Goal and Adaptation Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The uncertainty interaction problem in self-adaptive systems

et al. 2022

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“…The treatment of confidence in software models was already described in some of our previous works (Burgueño et al 2018;Bertoa, Burgueño, et al 2020;Muñoz et al 2020;Troya et al 2021;Burgueño et al 2022), and therefore we will not consider it further in this paper.…”

Section: Confidencementioning

confidence: 99%

Managing Reputation in Collaborative Social Computing Applications.

Moreno¹,

Pérez-Vereda²,

Vallecillo³

2022

JOT

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Reputation is a fundamental concept for making decisions about service providers. However, managing reputation in peer-to-peer distributed applications is not easy due to the lack of a central server that can compute this property from user opinions. Moreover, users have to marry this information with their individual trust in the service provider, which may be based on their past experiences, the opinions of their direct contacts, or both. This paper develops a reputation management system embedded in the Digital Avatars framework for collaborative social computing applications, using subjective logic. We show how the reputation of a given service provider can be calculated using the users' opinions about it, and how this reputation can be explicitly represented, managed and combined with the trust that individual service requesters may have in them, in order to make better informed decisions.

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“…Martin-Rodilla et al (Martín-Rodilla & Gonzalez-Perez 2019) propose the ConML language to annotate the elements of instance models with information representing the user's confidence in the truthfulness of that element. Burgueño et al (Burgueño, Clarisó, et al 2019;Burgueño et al 2022) propose an UML profile and its operationalization to express the degree of belief uncertainty that an agent has about the elements of an instance model using probabilities and subjective logic.…”

Section: Capturing Instance-level Uncertainty In Mbementioning

confidence: 99%

Managing Design-time Uncertainty in OCL Expressions.

Clarisó¹,

Burgueño²,

Cabot³

2022

JOT

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During the software design phase software models are created. These models must be eventually complete and correct. But achieving this state is challenging, and even more in early phases of development, where there are still plenty of unknown details about the system-to-be. This design-time uncertainty especially hinders the definition of those model elements that require a high level of precision: the system's business rules.Nevertheless, waiting for the requirements to be clear to build a complete model is not an efficient option, and often not even a feasible one as this could delay too much the whole project. Instead, uncertain models (i.e., models where only some details are present or are present up to a certain degree of certainty) can be used to advance other aspects of the development process (e.g., building a partial prototype that can help us explore architectural decisions) while, in parallel, we keep refining the models with the stakeholders. This paper proposes an extension to the OCL language to be able to specify uncertain OCL constraints and shows how we could operate with them to perform preliminary quality checks on the partial business rules. Moreover, we propose a prototype tool implementing this approach and discuss potential extensions.

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Dealing with Belief Uncertainty in Domain Models

Cited by 17 publications

References 52 publications

The uncertainty interaction problem in self-adaptive systems

The uncertainty interaction problem in self-adaptive systems

Managing Reputation in Collaborative Social Computing Applications.

Managing Design-time Uncertainty in OCL Expressions.

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