A transformation engine for model-driven UI generation

Popp, Roman; Falb, Jürgen; Raneburger, David; Kaindl, Hermann

doi:10.1145/2305484.2305532

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…At this time, the tools were able to generate GUIs, but with no particular emphasis yet on device tailoring. Related improvements and new techniques can be found, e.g., in [8,13,14,18,19,20,21]. This formal demonstration of our tool support for automated GUI generation has special emphasis on multi-device generation and the tailoring involved, which is new compared to the one at ACM IUI'09 [4].…”

Section: Introduction and Related Workmentioning

confidence: 92%

“…A model-transformation engine developed by ourselves performs the model-driven transformation of our Communication Models into Screen Models [13]. The major reason for developing our own engine was the need for certain tool features that we employ for achieving tailored (graphical) UIs in more specific situations (e.g., a specific small device).…”

Section: Gui Generation Using Model Transformationmentioning

confidence: 99%

“…To enable optimization for device-tailoring, comparing alternatives is mandatory [17]. Therefore, our (fully implemented) transformation engine for declarative rules is specifically designed for model-driven UI generation and optimization [13].…”

Section: Gui Generation Using Model Transformationmentioning

confidence: 99%

See 2 more Smart Citations

Tool support for automated multi-device GUI generation from discourse-based communication models

Popp

Raneburger

Kaindl

2013

Proceedings of the 5th ACM SIGCHI Symposium on Engineering Interactive Computing Systems

Self Cite

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Automated generation of graphical user interfaces (GUIs) from models is possible, but their usability is often not good enough for real-world use, in particular not for small devices. Also automated tailoring of GUIs for different devices is still an issue. Our tools provide such tailoring for different devices through automatic optimization of corresponding optimization objectives under given constraints. Currently, two different optimization strategies are implemented, with their focus on tapping and vertical scrolling on touchscreen, respectively. The constraints (relevant properties such as screen size and resolution) are to be provided by the users of our tools in device specifications. Through our tool support, WIMP (window, icon, menu, pointer) GUIs can be generated at a decent level of usability nearly automatically, in particular for small devices. This is important due to the more and more widespread use of smartphones.

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Section: Introduction and Related Workmentioning

confidence: 92%

Section: Gui Generation Using Model Transformationmentioning

confidence: 99%

See 1 more Smart Citation

Tool support for automated multi-device GUI generation from discourse-based communication models

Popp

Raneburger

Kaindl

2013

Proceedings of the 5th ACM SIGCHI Symposium on Engineering Interactive Computing Systems

Self Cite

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“…Heymann et al [18] used formal methods in proposing a systematic procedure for automatic generation of correct and succinct UIs. Several of the limitations of automatic UI generation have been described in detail [12,29,35]. Moreover, most of these techniques assume every user behaves in a similar and ideal manner, and therefore do not apply to novice users.…”

Section: Related Workmentioning

confidence: 99%

Edit distance modulo bisimulation

Zade

Adimoolam

Gollapudi

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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When a user learns to use a new device, her understanding of it evolves. A progressive comparison of the evolving user models towards the device target model, for analysing learning, involves determining the behavioral proximity between them. To quantify the gap between a user model and a target model, we introduce an edit distance metric for measuring their behavioral proximity using a bisimulation-based equivalence relation. We define edit distance to be the minimum number of edges and states with incident edges required to be deleted from and/or added to a user model to make it bisimilar to the target model. We propose an algorithm to compute edit distance between two models and employ the heuristic procedure on experimental data for computing edit distance between target and user models. The data is organised into two experiments depending on the device the user interacted with: (a) a simple device resembling a vending machine and (b) a close to real-world vehicle transmission model. The results validate our proposed metric as edit distance converges with progressive user learning, increases for erroneous learning, and remains unchanged indicating no learning.

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“…This framework includes a special-purpose transformation engine [12], which has been specifically designed for GUI generation, in contrast to general purpose languages like ATL or QVT. We conjecture that this facilitates its application in this context.…”

Section: Introductionmentioning

confidence: 99%

Model transformation rules for customization of multi-device graphical user interfaces

Raneburger

Kaindl

Popp

2015

Proceedings of the 7th ACM SIGCHI Symposium on Engineering Interactive Computing Systems

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In the context of model-driven generation of (graphical) user interfaces, systematic customization of automatically determined results is a challenge. While it may seem obvious that certain customizations can be expressed as specific transformation rules, adding such rules usually requires adaptation of existing rules and may lead to an unmanageable rule set.In this paper, we propose a new approach for managing model transformation rules for customization of graphical user interfaces in the context of their automated generation. Our approach facilitates managing transformation rules, because already existing rules do not have to be changed or replaced. A trial application provided some empirical evidence of the feasibility of our new approach. It even showed that some customization rules can be reused for generating graphical user interfaces for other devices with different properties, in effect for multi-device generation.

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A transformation engine for model-driven UI generation

Cited by 14 publications

References 11 publications

Tool support for automated multi-device GUI generation from discourse-based communication models

Tool support for automated multi-device GUI generation from discourse-based communication models

Edit distance modulo bisimulation

Model transformation rules for customization of multi-device graphical user interfaces

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