A taxonomy of uncertainty for dynamically adaptive systems

Ramírez,; Jensen,; Cheng, Betty H. C.

doi:10.1109/seams.2012.6224396

Cited by 129 publications

(98 citation statements)

References 34 publications

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“…For contextual requirements that are prone to an unpredictable environment (cr 1 and cr 4 ), precision, recall, and f-measure fluctuate more than for the other three contextual requirements over time. Recall of cr 1 is better than for cr 4 . This might be due to the fact that being on sea anchor allows the rowers to have an additional rest, resulting in patterns in the sensor data that measures the sleeping times, whereas the daily activities of the rowers when on sea anchor seem to be quite unpredictable (e.g., cleaning the boat, catching up with activities that they do not have time on regular days).…”

Section: Evaluation Of Data Mining (Rules)mentioning

confidence: 86%

“…In fact, uncertainty is the main reason that we cannot fully specify our knowledge of requirements and environment of a system during design time [4]. Due to uncertainty, self-adaptive systems need to evolve and even consider unforeseeable changes at runtime [11].…”

Section: Background and Related Workmentioning

confidence: 99%

“…While self-adaptivity promises to decrease the cost of handling the complexity of software systems at runtime [3], it challenges current software engineering practices, particularly the activities of requirements definition and satisfaction for systems that operate in uncertain environments [4]. Examples of such systems include software systems for smart cities that are interacting with thousands of individuals in a highly dynamic environment [5,6], and intelligent vehicle systems that have to deal with unforeseen tra c and weather conditions, as well as obstacles that have to be detected and avoided.…”

Section: Introductionmentioning

confidence: 99%

“…In such uncertain environments, runtime uncertainty [4] and unpredictability arise due to: (1) the environ-ment and the resulting limitations in accounting for all possible environmental conditions at design time, and 2) the monitoring infrastructure (e.g., sensor imprecision, sensor noise, and sensor failures). In this paper we address the impact that unpredictable environments have on a particular type of requirements, namely contextual requirements.…”

Section: Introductionmentioning

confidence: 99%

“…Unpredictability in the environment due to events or conditions that were unforeseeable at design time result in conditions not considered for a certain context for a contextual requirement [4]. In our example, the condition of the street being wet and the sun shining results in the user squinting his eyes.…”

Section: Introductionmentioning

confidence: 99%

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ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

Knauss

Damian

Franch

et al. 2016

Information and Software Technology

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Context: Runtime uncertainty such as unpredictable operational environment and failure of sensors that gather environmental data is a well-known challenge for adaptive systems. Objective: To execute requirements that depend on context correctly, the system needs up-to-date knowledge about the context relevant to such requirements. Techniques to cope with uncertainty in contextual requirements are currently underrepresented. In this paper we present ACon (Adaptation of Contextual requirements), a data-mining approach to deal with runtime uncertainty a↵ecting contextual requirements. Method: ACon uses feedback loops to maintain up-to-date knowledge about contextual requirements based on current context information in which contextual requirements are valid at runtime. Upon detecting that contextual requirements are a↵ected by runtime uncertainty, ACon analyzes and mines contextual data, to (re-)operationalize context and therefore update the information about contextual requirements. Results: We evaluate ACon in an empirical study of an activity scheduling system used by a crew of 4 rowers in a wild and unpredictable environment using a complex monitoring infrastructure. Our study focused on evaluating the data mining part of ACon and analyzed the sensor data collected onboard from 46 sensors and 90,748 measurements per sensor. Conclusion: ACon is an important step in dealing with uncertainty a↵ecting contextual requirements at runtime while considering end-user interaction. ACon supports systems in analyzing the environment to adapt contextual requirements and complements existing requirements monitoring approaches by keeping the requirements monitoring specification up-to-date. Consequently, it avoids manual analysis that is usually costly in today's complex system environments.

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Section: Evaluation Of Data Mining (Rules)mentioning

confidence: 86%

Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

Knauss

Damian

Franch

et al. 2016

Information and Software Technology

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Uncertainty handling in cyber–physical systems: State‐of‐the‐art approaches, tools, causes, and future directions

Asmat

Khan

Hussain

2022

J Software Evolu Process

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Cyber-Physical System (CPS) is the set of heterogeneous physical units linked to a network and performs complex operations to achieve a goal. Uncertainty increases with the increase in complexity of CPS. Thus, uncertainty needs to be mitigated to assure the quality and reliability of a CPS. This study aims to identify current stateof-the-art approaches, tools, root causes, and metrics for uncertainty in the domain of CPS. We performed a systematic literature review and employed keyword-based search on publisher sites to find potential studies. After applying the devised inclusion and exclusion criteria on identified potentially relevant studies, selection of studies is validated using an index engine. The core contributions of this study are (i) to categorize the tools used for uncertainty mitigation and existing root causes of uncertainty in CPS domain, (ii) to categorize the tools used for uncertainty mitigation and existing root causes of uncertainty in CPS domain, and (iii) to identify the stateof-the-art methods that lack the ability to elaborate the metrics to measure the uncertainty in CPS. The results of the proposed study are beneficial in guiding future research on devising new approaches or tools to mitigate the causes of uncertainty in CPS.

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Toward the adaptation of component‐based architectures by model transformation: behind smart user interfaces

et al. 2014

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SUMMARYGraphical user interfaces are not always developed for remaining static. There are GUIs with the need of implementing some variability mechanisms. Component-based GUIs are an ideal target for incorporating this kind of operations, because they can adapt their functionality at run-time when their structure is updated by adding or removing components or by modifying the relationships between them. Mashup user interfaces are a good example of this type of GUI, and they allow to combine services through the assembly of graphical components. We intend to adapt component-based user interfaces for obtaining smart user interfaces. With this goal, our proposal attempts to adapt abstract component-based architectures by using model transformation. Our aim is to generate at run-time a dynamic model transformation, because the rules describing their behavior are not pre-set but are selected from a repository depending on the context. The proposal describes an adaptation schema based on model transformation providing a solution to this dynamic transformation. Context information is processed to select at run-time a rule subset from a repository. Selected rules are used to generate, through a higher-order transformation, the dynamic model transformation. This approach has been tested through a case study which applies different repositories to the same architecture and context. Moreover, a web tool has been developed for validation and demonstration of its applicability. The novelty of our proposal arises from the adaptation schema that creates a non pre-set transformation, which enables the dynamic adaptation of component-based architectures.

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A taxonomy of uncertainty for dynamically adaptive systems

Cited by 129 publications

References 34 publications

ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

Uncertainty handling in cyber–physical systems: State‐of‐the‐art approaches, tools, causes, and future directions

Toward the adaptation of component‐based architectures by model transformation: behind smart user interfaces

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