Living with Uncertainty in Model-Based Development

Bernardi, Simona; Famelis, Michalis; Jézéquel, Jean-Marc; Mirandola, Raffaela; Palacin, Diego Perez; Polack, Fiona; Trubiani, Catia

doi:10.1007/978-3-030-81915-6_8

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…Thus, a number of techniques explicitly integrating uncertainty in modeling recently emerged in different stages of the engineering life-cycle, such as design-time specification [30], testing [31][32][33], and runtime verification [6]. The mainstream approach to express uncertainty is by using model parameters, that are then handled using alternative methods including: probabilistic methods [34], where parameters are described by probability density functions; fuzzy sets [35], in which uncertain parameters are described with fuzzy boundaries; and interval analysis that aim at studying the limits of variation to detect either best/worst cases or violated requirements [3,6,7].…”

Section: Related Workmentioning

confidence: 99%

“…Research on uncertainty understanding, quantification and mitigation in software-intensive and (self-) adaptive systems has been the focus of several work in the last few years (e.g., [1][2][3]). Mainstream approaches to represent and handle uncertainty involve the enhancement of the analysis and plan activities of a feedback control loop architecture for adaptation (e.g., the well known MAPE-K loop [4,5]) with model-based inference techniques in order to make predictions about future observations and plan actuation.…”

Section: Introductionmentioning

confidence: 99%

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Taming model uncertainty in self-adaptive systems using bayesian model averaging

Camilli

Mirandola

Scandurra

2022

Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems

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Research on uncertainty quantification and mitigation of softwareintensive systems and (self-)adaptive systems, is increasingly gaining momentum, especially with the availability of statistical inference techniques (such as Bayesian reasoning) that make it possible to mitigate uncertain (quality) attributes of the system under scrutiny often encoded in the system model in terms of model parameters. However, to the best of our knowledge, the uncertainty about the choice of a specific system model did not receive the deserved attention.This paper focuses on self-adaptive systems and investigates how to mitigate the uncertainty related to the model selection process, that is, whenever one model is chosen over plausible alternative and competing models to represent the understanding of a system and make predictions about future observations. In particular, we propose to enhance the classical feedback loop of a self-adaptive system with the ability to tame the model uncertainty using Bayesian Model Averaging. This method improves the predictions made by the analyze component as well as the plan that adopts metaheuristic optimizing search to guide the adaptation decisions. Our empirical evaluation demonstrates the cost-effectiveness of our approach using an exemplar case study in the robotics domain. CCS CONCEPTS• Software and its engineering → Software system models; Software functional properties; • Computer systems organization → Self-organizing autonomic computing.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Taming model uncertainty in self-adaptive systems using bayesian model averaging

Camilli

Mirandola

Scandurra

2022

Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems

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“…In (Bernardi et al 2021), irreducible uncertainty is elaborated as phenomena that are inherently unknowable -uncertainty that persists even in the presence of complete information. Reducible uncertainty can, however, be resolved by improved understanding.…”

Section: Uncertainty Modellingmentioning

confidence: 99%

“…A first potential source of uncertainty is in the artefacts (e.g. models) being manipulated (Bernardi et al 2021). A second potential source of uncertainty is in the processes that manipulate artefacts.…”

Section: Categorising Sources Of Uncertainty In Wfmentioning

confidence: 99%

Interface Contracts for Workflow+ Models: an Analysis of Uncertainty across Models.

Paige¹,

Polack²,

Zschaler³

et al. 2022

JOT

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Workflow models are used to rigorously specify and reason about diverse types of processes. The Workflow+ (WF + ) framework has been developed to support unified modelling of the control and data in processes that can be used to derive assurance cases that support certification. However, WF + is limited in its support for precise contracts on workflow models, which can enable powerful forms of static analysis and reasoning. In this paper we propose a mechanism for adding interface contracts to WF + models, which can thereafter be applied to tracing and reasoning about the uncertainty that arises when combining heterogeneous models. We specifically explore this in terms of design models and assurance case models.We argue that some of the key issues in managing some types of uncertainty can be partly addressed by use of interface contracts.

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“…But, in addition to the issues discussed in this article, I believe that we must be aware of other replicability problems related to software and hardware architectures. The question of uncertainty in computing has been studied by computer scientists, explaining differences that can be obtained even for very simple codes when changing software versions, or when changing the hardware (e.g., running on a 32-or 64-bit processor) [4]. It has also been shown that these uncertainties are not independent.…”

mentioning

confidence: 99%

Open and Reproducible Science: Desirable or Even Mandatory, But Not So Simple! [From the Editor]

Jutten¹

2023

IEEE Signal Process. Mag.

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Living with Uncertainty in Model-Based Development

Cited by 5 publications

References 26 publications

Taming model uncertainty in self-adaptive systems using bayesian model averaging

Taming model uncertainty in self-adaptive systems using bayesian model averaging

Interface Contracts for Workflow+ Models: an Analysis of Uncertainty across Models.

Open and Reproducible Science: Desirable or Even Mandatory, But Not So Simple! [From the Editor]

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