Self-adaptive UIs: Integrated Model-Driven Development of UIs and Their Adaptations

Yigitbas, Enes; Stahl, Hagen; Sauer, Stefan; Engels, Gregor

doi:10.1007/978-3-319-61482-3_8

Cited by 22 publications

(16 citation statements)

References 18 publications

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“…Beside ContextML, we developed another domain-specific language, AdaptML, introduced in [39], which supports the modeling of UI adaptations through UI adaptation rules. AdaptML is designed as a complementary modeling language to OMG's core UI modeling language IFML and allows domain experts, for example, Web designers, to model adaptation concerns by specifying the conditions and actions for UI adaptations.…”

Section: Integrated Modeling Environmentmentioning

confidence: 99%

Integrated model-driven development of self-adaptive user interfaces

et al. 2020

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Modern user interfaces (UIs) are increasingly expected to be plastic, in the sense that they retain a constant level of usability, even when subjected to context changes at runtime. Self-adaptive user interfaces (SAUIs) have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. The development of SAUIs is a challenging and complex task as additional aspects like context management and UI adaptation have to be covered. In classical model-driven UI development approaches, these aspects are not fully integrated and hence introduce additional complexity as they represent crosscutting concerns. In this paper, we present an integrated model-driven development approach where a classical model-driven development of UIs is coupled with a model-driven development of context-of-use and UI adaptation rules. We base our approach on the core UI modeling language IFML and introduce new modeling languages for contextof-use (ContextML) and UI adaptation rules (AdaptML). The generated UI code, based on the IFML model, is coupled with the context and adaptation services, generated from the ContextML and AdaptML model, respectively. The integration of the generated artifacts, namely UI code, context, and adaptation services in an overall rule-based execution environment, enables runtime UI adaptation. The benefit of our approach is demonstrated by two case studies, showing the development of SAUIs for different application scenarios and a usability study which has been conducted to analyze end-user satisfaction of SAUIs.

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Section: Integrated Modeling Environmentmentioning

confidence: 99%

Integrated model-driven development of self-adaptive user interfaces

et al. 2020

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“…In another example, Yigitbas et al [156] presented a method of self-adaptation and context management in the development of user interfaces (UI) to ease user acceptance of technology. The method includes a development path, which takes care of the model-driven development of UIs.…”

Section: ) User Experiencementioning

confidence: 99%

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

et al. 2019

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Autonomous vehicles are becoming a reality in places with advanced infrastructure to support their operations. In crowded places, harsh environments, missions that require these vehicles to be aware of the context in which they are operating, and situations requiring continuous coordination with humans such as in disaster relief, advanced-vehicle systems (AVSs) need to be better contextually aware. The vast literature referring to ''context-aware systems'' is still sparse, focusing on very limited forms of contextual awareness. It requires a structured approach to bring it together to truly realize contextual awareness in AVSs. This paper uses a human-AVSs (HAVSs) lens to polarize the literature in a coherent form suitable for designing distributed HAVSs. We group the relevant literature into two categories: contextual-awareness related to the vehicle infrastructure itself that enables AVSs to operate, and contextual-awareness related to HAVSs. The former category focuses on the communication backbone for AVSs including ad-hoc networks, services, wireless communication, radio systems, and the cyber security and privacy challenges that arise in these contexts. The latter category covers recommender systems, which are used to coordinate the actions that sit at the interface of the human and AVSs, human-machine interaction issues, and the activity recognition systems as the enabling technology for recommender systems to operate autonomously. The structured analysis of the literature has identified a number of open research questions and opportunities for further research in this area.

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“…Our OTF usability evaluation solution targets rule-based UI adaptation approaches as introduced in our previous work [5]. We extended our existing rulebased UI adaptation approach with capabilities to support continuous context monitoring and collect context-driven instant user feedback.…”

Section: Solution Ideamentioning

confidence: 99%

On-the-Fly Usability Evaluation of Mobile Adaptive UIs Through Instant User Feedback

Yigitbas

Jovanovikj

Josifovska

et al. 2019

Human-Computer Interaction – INTERACT 2019

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Adaptive User Interfaces (AUIs) have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. For the acceptance of AUIs, usability plays a crucial role. Classical usability evaluation methods like usability tests, interviews or cognitive walkthroughs are not sufficient for evaluating end-user satisfaction of AUIs at runtime. The main reason is that the UI and context-of-use are dynamically changing and acceptance of UI adaptation features has to be evaluated in the moment and contextof-use when the adaptations occur. To address this challenge, we present an on-the-fly usability evaluation solution that combines continuous context monitoring together with collection of instant user feedback to assess end-user satisfaction of UI adaptation features. We demonstrate the usefulness and benefit of our solution based on a mobile Android mail app, which served as basis for conducting a usability study.

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Self-adaptive UIs: Integrated Model-Driven Development of UIs and Their Adaptations

Cited by 22 publications

References 18 publications

Integrated model-driven development of self-adaptive user interfaces

Integrated model-driven development of self-adaptive user interfaces

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

On-the-Fly Usability Evaluation of Mobile Adaptive UIs Through Instant User Feedback

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