A Survey of Model Driven Engineering Tools for User Interface Design

Pérez-Medina, Jorge Luis; Dupuy-Chessa, Sophie; Front, Agnès

doi:10.1007/978-3-540-77222-4_8

Cited by 35 publications

(18 citation statements)

References 20 publications

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“…There have been a number of surveys of MDE tools (e.g., [7][8][9]) but they focus on classifying tools based on what technical functionalities they provide. More recently, Paige and Varró report on lessons learned from developing two significant (academic) MDE tools [10].…”

Section: Introductionmentioning

confidence: 99%

Industrial Adoption of Model-Driven Engineering: Are the Tools Really the Problem?

Whittle

Hutchinson

Rouncefield

et al. 2013

Lecture Notes in Computer Science

102

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Section: Introductionmentioning

confidence: 99%

Industrial Adoption of Model-Driven Engineering: Are the Tools Really the Problem?

Whittle

Hutchinson

Rouncefield

et al. 2013

Lecture Notes in Computer Science

102

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“…A survey on model-driven engineering tools for developing UIs [Pérez-Medina et al 2007] covered many existing tools including: ACCELEO, AndroMDA, ADT, AToM3, DSL Tools, Kermeta, ModFact, Merlin, MDA Workbench, MOFLON, OptimalJ, QVT Partners, SmartQVT, and UMLX. One of the conclusions made was that these tools are centered on MOF hence support the creation of domain-specific models.…”

Section: Tools Supporting Adaptive Model-driven User Interface Develomentioning

confidence: 99%

Adaptive Model-Driven User Interface Development Systems

2014

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Adaptive user interfaces (UIs) were introduced to address some of the usability problems that plague many software applications. Model-driven engineering formed the basis for most of the systems targeting the development of such UIs. An overview of these systems is presented and a set of criteria is established to evaluate the strengths and shortcomings of the state-of-the-art, which is categorized under architectures, techniques, and tools. A summary of the evaluation is presented in tables that visually illustrate the fulfillment of each criterion by each system. The evaluation identified several gaps in the existing art and highlighted the areas of promising improvement. The user interface (UI) layer is considered one of the key components of software applications since it connects their end-users to the functionality. Well-engineered and robust software applications could eventually fail to be adopted due to a weak UI layer. Some user interface development techniques such as: universal design [Mace et al. 1990], inclusive design [Keates et al. 2000], and design for all [Stephanidis 1997] promote the concept of making one UI design fit as many people as possible. Yet, a UI is not independent from its context-of-use, which is defined in terms of the user, platform, and environment [Calvary et al. 2003]. The "one design fits all" approach is unable to accommodate all the cases of variability in the context-of-use, in many cases leading to a diminished user experience. Building multiple UIs for the same functionality due to context variability is difficult since the scope of variability cannot be completely known at design-time and there is a high cost incurred by manually developing multiple versions of the UI. Adaptive UIs have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. User interfaces capable of adapting to their context-of-use are also referred to as multi-context or multi-target [Fonseca 2010]. A key goal behind adaptive UIs is plasticity denoting a UI's ability to preserve its usability across multiple contexts-of-use [Coutaz 2010]. Norcio and Stanley [1989] consider that the idea of an adaptive UI is straightforward since it simply means that: "The interface should adapt to the user; rather than the user adapting to the system" (p. 399) but they note that in spite of the simplicity of the definition, there are some difficult and complex problems relating to adaptive UIs. In our study of the literature, we noticed that some of these problems are technical and are related to devising systems that can support the development of adaptive UIs, while others are related to human factors such as the end-user acceptance of these UIs. Realizing the abstract properties illustrated in Fig. 1, could help in handling some of the technical and human problems related to adaptive UIs. Salehie and Tahvildari [2009] present a hierarchy of adaptability properties for software systems, referred to as self-* properties. This hierarchy demon...

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“…Model-driven development (MDD) [29] suggests that a model is the source of information, and the resulting source code is generated using this model together with a set of transformation rules. The main advantage should be reduction of information restatement that must be handled manually [9] for different perspectives.…”

Section: A Ui Design Approachesmentioning

confidence: 99%

Efficient Description and Cache Performance in Aspect-Oriented User Interface Design

Černý¹,

Macík

Donahoo

et al. 2014

Annals of Computer Science and Information Systems

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Abstract-Increasing demands on web user interface (UI) usability, adaptability, and dynamic behavior drives ever growing development and maintenance complexity. Conventional design approaches scale poorly with such rising complexity, resulting in rapidly increasing costs. Much of the complexity centers around data presentation and processing. Recent work greatly reduces such data complexity through the application of AspectOriented UI (AOUI) design, which separates various UI concerns; however, rendering in conventional and even AOUI approaches fails to maintain this separation, often resulting in high repetitions of concern fragments due to tangling. Even worse, mixing of dynamic and immutable components greatly limits caching efficacy as each have differing lifetimes. We extend AOUI design to push down concern separation to rendering, which reduces description size, through repetition reduction, and enables separate caching of individual concerns. Our results show considerable size reduction of UI descriptions for data presentations, faster load times and extended caching capabilities.

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A Survey of Model Driven Engineering Tools for User Interface Design

Cited by 35 publications

References 20 publications

Industrial Adoption of Model-Driven Engineering: Are the Tools Really the Problem?

Industrial Adoption of Model-Driven Engineering: Are the Tools Really the Problem?

Adaptive Model-Driven User Interface Development Systems

Efficient Description and Cache Performance in Aspect-Oriented User Interface Design

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