Synthesis of probabilistic models for quality-of-service software engineering

Gerasimou, Simos; Călinescu, Radu; Tamburrelli, Giordano

doi:10.1007/s10515-018-0235-8

Cited by 58 publications

(63 citation statements)

References 96 publications

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“…Over the past years, the number of studies that take into account uncertainty in self-adaptive systems has gradually been increasing. A typical example is the use of probabilistic runtime models, such as Markov decision processes [16], [17] and parametric stochastic models [18], [19] to reason about change when making adaptation decisions. As progress is taking place, a more systematic understanding of SAS uncertainty is required.…”

Section: I R E L At E D S T U D I E Smentioning

confidence: 99%

Understanding Uncertainty in Self-adaptive Systems

Călinescu

Mirandola

Pérez-Palacín

et al. 2020

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

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Ensuring that systems achieve their goals under uncertainty is a key driver for self-adaptation. Nevertheless, the concept of uncertainty in self-adaptive systems (SAS) is still insufficiently understood. Although several taxonomies of uncertainty have been proposed, taxonomies alone cannot convey the SAS research community's perception of uncertainty. To explore and to learn from this perception, we conducted a survey focused on the SAS ability to deal with unanticipated change and to model uncertainty, and on the major challenges that limit this ability. In this paper, we analyse the responses provided by the 51 participants in our survey. The insights gained from this analysis include the view-held by 71% of our participants-that SAS can be engineered to cope with unanticipated change, e.g., through evolving their actions, synthesising new actions, or using default actions to deal with such changes. To handle uncertainties that affect SAS models, the participants recommended the use of confidence intervals and probabilities for parametric uncertainty, and the use of multiple models with model averaging or selection for structural uncertainty. Notwithstanding this positive outlook, the provision of assurances for safety-critical SAS continues to pose major challenges according to our respondents. We detail these findings in the paper, in the hope that they will inspire valuable future research on self-adaptive systems.

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Section: I R E L At E D S T U D I E Smentioning

confidence: 99%

Understanding Uncertainty in Self-adaptive Systems

Călinescu

Mirandola

Pérez-Palacín

et al. 2020

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

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“…Ideally, these techniques and tools would start from a set of SCPS requirements, and would present developers with alternative systems designs that satisfy the requirements and are Pareto-optimal with respect to multiple optimisation criteria such as cost, utility and environmental impact. Such solutions have been proposed recently for the development of software systems (e.g., [24], [25]), and can also handle uncertainty in the operational profile of the system under development [26], [27]. However, these solutions cannot yet handle some of the new types of requirements encountered in SCPS.…”

Section: Oc2) Ensuring the Accuracy Of Stochastic Models Of Scps-mentioning

confidence: 99%

Socio-Cyber-Physical Systems: Models, Opportunities, Open Challenges

Călinescu¹,

Cámara

Paterson³

2019

2019 IEEE/ACM 5th International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS)

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Almost without exception, cyber-physical systems operate alongside, for the benefit of, and supported by humans. Unsurprisingly, disregarding their social aspects during development and operation renders these systems ineffective. In this paper, we explore approaches to modelling and reasoning about the human involvement in socio-cyber-physical systems (SCPS). To provide an unbiased perspective, we describe both the opportunities afforded by the presence of human agents, and the challenges associated with ensuring that their modelling is sufficiently accurate to support decision making during SCPS development and, if applicable, at run-time. Using SCPS examples from emergency management and assisted living, we illustrate how recent advances in stochastic modelling, analysis and synthesis can be used to exploit human observations about the impact of natural and man-made disasters, and to support the efficient provision of assistive care.

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“…For small models it is possible to run an exhaustive search of the solution space, however, as the number of locations and actions increases this becomes unfeasible and therefore we look to more efficient search heuristics. For our example the search heuristic was implemented using the EvoChecker approach which we previously developed [3], [25], [26].…”

Section: Planningmentioning

confidence: 99%

Using Unstructured Data to Improve the Continuous Planning of Critical Processes Involving Humans

Paterson

Călinescu

Wang

et al. 2019

2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

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The success of processes executed in uncertain and changing environments is reliant on the dependable use of relevant information to support continuous planning at runtime. At the core of this planning is a model which, if incorrect, can lead to failures and, in critical processes such as evacuation and disaster relief operations, to harm to humans. Obtaining reliable and timely estimations of model parameters is often difficult, and considerable research effort has been expended to derive methods for updating models at run-time. Typically, these methods use data sources such as system logs, run-time events and sensor readings, which are well structured. However, in many critical processes, the most relevant data are produced by human participants to, and observers of, the process and its environment (e.g., through social media) and is unstructured. For such scenarios we propose COPE, a work-in-progress method for the continuous planning of critical processes involving humans and carried out in uncertain, changing environments. COPE uses a combination of runtime natural-language processing (to update a stochastic model of the target process based on unstructured data) and stochastic model synthesis (to generate Pareto-optimal plans for the process). Preliminary experiments indicate that COPE can support continuous planning effectively for a simulated evacuation operation after a natural disaster. Index Terms-natural-language processing; stochastic model synthesis; probabilistic model checking

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Synthesis of probabilistic models for quality-of-service software engineering

Cited by 58 publications

References 96 publications

Understanding Uncertainty in Self-adaptive Systems

Understanding Uncertainty in Self-adaptive Systems

Socio-Cyber-Physical Systems: Models, Opportunities, Open Challenges

Using Unstructured Data to Improve the Continuous Planning of Critical Processes Involving Humans

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