Alfonso García Frey

QUIMERA: a quality metamodel to improve design rationale

With the increasing complexity of User Interfaces (UI) it is more and more necessary to make users understand the UI. We promote a Model-Driven approach to improve the perceived quality through an explicit and observable design rationale. The design rationale is the logical reasons given to justify a designed artifact. The design decisions are not taken arbitrarily, but following some criteria. We propose a Quality Metamodel to justify these decisions along a Model-Driven Engineering approach.

Self-explanatory user interfaces by model-driven engineering

Modern User Interfaces (UI) must deal with the increasing complexity of applications in terms of functionality as well as new properties as plasticity. The plasticity of a UI denotes its capacity of adaptation to the context of use preserving its quality. The efforts in plasticity have focused on the (meta) modeling of the UI, but the quality remains uncovered. We suggest a method for improving the quality of the UIs by providing explanations about the design of the UI itself: this is, by the use of the Self-Explanation. Self-Explanatory User Interfaces (SEUI) makes reference to the capacity of a UI to supply the end-user with all the information on the rational of the UI, about its constitution (for example, what is the purpose of this button?), its current state (why is the menu disabled?) as well as its evolution (how can I enable this feature?). This thesis investigates the SEUI by Model Driven Engineering (MDE), where models are kept at run-time allowing the necessary techniques that maintain this link between design and execution.

Model Transformation Configuration and Variability Management for User Interface Design

User Interfaces (UI) design is a complex and multi-faceted problem, owing to the ever increasing variability of the design options and the interaction context (devices, user profiles, and their environment). Moreover, UI design choices stand on users’ needs elicitation, which are difficult to evaluate precisely upfront and require iterative design cycles based on trial and error. All this complex variability should be managed efficiently to ensure moderate design costs. In this article, we propose a variability management approach integrated into a UI rapid prototyping process, which involves the combination of Model-Driven Engineering (MDE) and Software Product Lines. Our approach supports the separation of concerns through multi-step partial configuration of UI features, enabling each stakeholder of the UI design process to define the variability on the assets she manages. We have implemented this approach in our existing MDE UI generation Framework.

AME: an adaptive modelling environment as a collaborative modelling tool

The development of User Interfaces (UIs) is a complex task. Researches shown that one of the reasons is the lack of integrated views that often forces developers to implement suboptimal solutions. These integrated views refer to (1) the artifacts that are manipulated by the stakeholders during the UI development process and (2) how these artifacts relate to each other. To overcome the lack of integrated views in the context of model-based UI development this paper introduces AMEs, Adaptive Modelling Environments that support UI development by providing explicit representations of both the artifacts and their relations. A first prototype is depicted in a case study and illustrated with a video. Details of the architecture are provided.

Towards a multi-stakehoder engineering approach with adaptive modelling environments

Human-Computer Interaction (HCI) addresses the study, planning and design of the interaction between people and computers through User Interfaces (UIs). The co-design and co-development of these UIs involve different stakeholders as Developers, Functional Analysts, Usability Experts and Interaction designers among others, all of them responsible for different UI elements (respectively implementation, functional requirements, usability and interaction workflow). Collaboration between stakeholders has been identified as a keyfactor for UI development. This article investigates how concepts and methods from model-driven engineering (MDE) can contribute to UI development through a collaborative approach. We discuss how UI views (extra-UI, mega-UI) can be useful for multi-stakeholder engineering, and how MDE acts as the backbone that supports them. The global approach is implemented through a first prototype of an Adaptive Modelling Environment (AME) illustrated through a case study. A screencast of the tool is also provided.

A multi-viewpoint approach to support collaborative user interface generation

Human-Computer Interaction (HCI) addresses the study, planning and design of the interaction between people and computers through User Interfaces (UIs). The co-design and co-development of these UIs involve different stakeholders as Developers, Functional Analysts, Usability Experts among others, all of them responsible for different UI elements (respectively implementation, functional requirements, usability). Collaboration between stakeholders has been identified as a key factor for UI design and development. This article proposes a multi-viewpoint approach to support collaboration in a UI co-design and co-development process. The article discusses the process itself and the different viewpoints manipulated by all the involved stakeholders. We propose an implementation of our approach in a model-based multi-viewpoint prototype. We show the feasibility of this approach implementing it on a case-study and a multi-viewpoint prototype.

Model-Based Self-explanatory UIs for Free, but Are They Valuable?

Model-Driven Engineering (MDE) has been extensively used for generating User Interfaces (UIs) from models. As long as these models are kept alive at runtime, the UIs are capable of adapting to variations of the context of use. This paper investigates a potentially powerful side effect: the possibility of enriching the UIs with explanations directly generated from these models. This paper first describes a software infrastructure that supports this generation of explanations. It then reports on a user study that evaluates the added value of such model based self-explanations.

Variability Management and Assessment for User Interface Design

User Interface (UI) design remains an open, wicked, complex and multi-faceted problem, owing to the ever increasing variability of design options resulting from multiple contexts of use, i.e., various end-users, heterogeneous devices and computing platforms, as well as their varying environments. Designing multiple UIs for multiple contexts of use inevitably requires an ever growing amount of time and resources that not all organizations are able to afford. Moreover, UI design choices stand on end-users’ needs elicitation, which are recognized to be difficult to evaluate precisely upfront and which require iterative design cycles. All this complex variability should be managed efficiently to maintain time and resources to an acceptable level. To address these challenges, this article proposes a variability management approach integrated into a UI rapid prototyping process, which involves the combination of Model-Driven Engineering, Software Product Lines and Interactive Genetic Algorithms.

Variability management supporting the model-driven design of user interfaces

User Interfaces (UI) design is a complex and multi-faceted problem. It depends on devices, users and their environments and involves various stakeholders with different backgrounds. Moreover, user requirements are difficult to evaluate precisely upfront and require iterative design cycles based on trial and error. All this variability is complex and should be managed efficiently to ensure moderate design costs. One solution is to implement in the UI design process Model-Driven Engineering (MDE) and Software Product Lines (SPL). However, current SPL approaches do not consider problems related to specific UI design models, notably the many concerns underlying them. We propose an SPL approach that supports the separation of concerns through multi-step partial configuration of UI features. The approach is implemented in our existing MDE UI generation framework.

Towards Estimating and Predicting User Perception on Software Product Variants

Estimating and predicting user subjective perceptions on software products is a challenging, yet increasingly important, endeavour. As an extreme case study, we consider the problem of exploring computer-generated art object combinations that will please the maximum number of people. Since it is not feasible to gather feedbacks for all art products because of a combinatorial explosion of possible configurations as well as resource and time limitations, the challenging objective is to rank and identify optimal art product variants that can be generated based on their average likability. We present the use of Software Product Line (SPL) techniques for gathering and leveraging user feedbacks within the boundaries of a variability model. Our approach is developed in two phases: 1) the creation of a data set using a genetic algorithm and real feedback and 2) the application of a data mining technique on this data set to create a ranking enriched with confidence metrics. We perform a case study of a real-world computer-generated art system. The results of our approach on the arts domain reveal interesting directions for the analysis of user-specific qualities of SPLs.