Engineering Adaptive Model-Driven User Interfaces for Enterprise Applications

Enterprise applications such as enterprise resource planning systems have numerous complex user interfaces (UIs). Usability problems plague these UIs because they are offered as a generic off-the-shelf solution to end-users with diverse needs in terms of their required features and layout preferences. Adaptive UIs can help in improving usability by tailoring the features and layout based on the context-of-use. The model-driven UI development approach offers the possibility of applying different types of adaptations on the various UI levels of abstraction. This approach forms the basis for many works researching the development of adaptive UIs. Yet, several gaps were identified in the state-of-the-art adaptive model-driven UI development systems. To fill these gaps, this thesis presents an approach that offers the following novel contributions: - The Cedar Architecture serves as a reference for developing adaptive model-driven enterprise application user interfaces. - Role-Based User Interface Simplification (RBUIS) is a mechanism for improving usability through adaptive behavior, by providing end-users with a minimal feature-set and an optimal layout based on the context-of-use. - Cedar Studio is an integrated development environment, which provides tool support for building adaptive model-driven enterprise application UIs using RBUIS based on the Cedar Architecture. The contributions were evaluated from the technical and human perspectives. Several metrics were established and applied to measure the technical characteristics of the proposed approach after integrating it into an open-source enterprise application. Additional insights about the approach were obtained through the opinions of industry experts and data from real-life projects. Usability studies showed the approach’s ability to significantly improve usability in terms of end-user efficiency, effectiveness and satisfaction

Using interpreted runtime models for devising adaptive user interfaces of enterprise applications

Although proposed to accommodate new technologies and the continuous evolution of business processes and business rules, current model-driven approaches do not meet the flexibility and dynamic needs of feature-rich enterprise applications. This paper illustrates the use of interpreted runtime models instead of static models or generative runtime models, i.e. those that depend on code generation. The benefit of interpreting runtime models is illustrated in two enterprise user interface (UI) scenarios requiring adaptive capabilities. Concerned with devising a tool-supported methodology to accommodate such advanced adaptive user interface scenarios, we propose an adaptive UI architecture and the meta-model for such UIs. We called our architecture Custom Enterprise Development Adaptive Architecture (CEDAR). The applicability and performance of the proposed approach are evaluated by a case study

Engineering Adaptive Model-Driven User Interfaces

Software applications that are very large-scale, can encompass hundreds of complex user interfaces (UIs). Such applications are commonly sold as feature-bloated off-the-shelf products to be used by people with variable needs in the required features and layout preferences. Although many UI adaptation approaches were proposed, several gaps and limitations including: extensibility and integration in legacy systems, still need to be addressed in the state-of-the-art adaptive UI development systems. This paper presents Role-Based UI Simplification (RBUIS) as a mechanism for increasing usability through adaptive behaviour by providing end-users with a minimal feature-set and an optimal layout, based on the context-of- use. RBUIS uses an interpreted runtime model-driven approach based on the Cedar Architecture, and is supported by the integrated development environment (IDE), Cedar Studio. RBUIS was evaluated by integrating it into OFBiz, an open-source ERP system. The integration method was assessed and measured by establishing and applying technical metrics. Afterwards, a usability study was carried out to evaluate whether UIs simplified with RBUIS show an improvement over their initial counterparts. This study leveraged questionnaires, checking task completion times and output quality, and eye-tracking. The results showed that UIs simplified with RBUIS significantly improve end-user efficiency, effectiveness, and perceived usability

Adaptive model-driven user interface development systems

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

EUD-MARS: End-User Development of Model-Driven Adaptive Robotics Software Systems

Empowering end-users to program robots is becoming more significant. Introducing software engineering principles into end-user programming could improve the quality of the developed software applications. For example, model-driven development improves technology independence and adaptive systems act upon changes in their context of use. However, end-users need to apply such principles in a non-daunting manner and without incurring a steep learning curve. This paper presents EUD-MARS that aims to provide end-users with a simple approach for developing model-driven adaptive robotics software. End-users include people like hobbyists and students who are not professional programmers but are interested in programming robots. EUD-MARS supports robots like hobby drones and educational humanoids that are available for end-users. It offers a tool for software developers and another one for end-users. We evaluated EUD-MARS from three perspectives. First, we used EUD-MARS to program different types of robots and assessed its visual programming language against existing design principles. Second, we asked software developers to use EUD-MARS to configure robots and obtained their feedback on strengths and points for improvement. Third, we observed how end-users explain and develop EUD-MARS programs, and obtained their feedback mainly on understandability, ease of programming, and desirability. These evaluations yielded positive indications of EUD-MARS

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Visual Simple Transformations: Empowering End-Users to Wire Internet of Things Objects

Empowering end-users to wire Internet of Things (IoT) objects (things and services) together would allow them to more easily conceive and realize interesting IoT solutions. A challenge lies in devising a simple end-user development approach to support the specification of transformations, which can bridge the mismatch in the data being exchanged among IoT objects. To tackle this challenge, we present Visual Simple Transformations (ViSiT) as an approach for end-users to specify such transformations by using a jigsaw puzzle metaphor, which get automatically converted into an underlying executable workflow. ViSiT is explained by presenting meta-models and an architecture that serves as a reference for implementing a system of connected IoT objects. A tool is provided for supporting end-users in visually developing and testing transformations. A tool is also provided for allowing software developers to modify, if they wish, a transformation’s underlying implementation. This work was evaluated from a technical perspective by developing transformations and measuring ViSiT’s efficiency and scalability and by constructing an example application to show ViSiT’s practicality. A study was conducted to evaluate this work from an end-user perspective, and its results showed positive indications of perceived usability, learnability, and the ability to conceive real-life scenarios for ViSiT

A Systematic Framework For Assessing The Implementation Phase Of Enterprise Resource Planning Systems

Enterprise Resource Planning (ERP) systems are a major pillar in the management of evolving modern businesses. With the continuous change of technology and increase of business process complexity, ERP systems had to evolve drastically to accommodate the needs of modern businesses. This makes the implementation of such systems very complex hence increasing the risk of failure. Aiming to reducing such risks and protecting businesses as well as ERP vendors from financial losses, this paper proposes a set of categorized critical success factors (CSFs) for assessing ERP implementations. A support tool is also presented to visualize the assessments of the current and past implementation states to help in monitoring the implementation's evolution history