End-user development challenges include configuring internet-of-things (IoT) devices and services to work together and programming robots for different purposes. Millions of school kids nowadays are taught programming concepts using end-user development environments. Can’t we use a similar environment to empower end-users to develop software for IoT and robotics? Visual Simple Transformations (ViSiT) empowers end-users to wire IoT devices and services. For example, end-users can use puzzle pieces to implement a transformation that allows a Microsoft Xbox controller to communicate with a Lego Mindstorms robot. EUD-MARS provides end-users with a simple approach to developing model-driven adaptive robotics software using a visual programming language.

Models are often used to represent various types of systems. This is especially true for software systems, where cooperating teams create models using a modeling language (e.g., UML). In cooperative modeling scenarios, it is useful to identify contributions and changes performed by individuals and teams. CHECKSUM monitors the cooperative work done on models and maintains an immutable changelog. CHECKSUM uses its changelog to measure contributions based on points, time, and quality, and to enable the auditing of a model’s change history. GEneric Meta-Model (GEMM) enables CHECKSUM to support an extensible variety of model types by unifying the models’ underlying representation.

Contextual Help for Adaptive INterfaces (CHAIN) is an approach for developing model-driven contextual help that maintains its usefulness across UI adaptations. This trait is achieved by interpreting the help models at runtime and overlaying instructions on the running adapted UI. A language called Contextual Help for Adaptive INterfaces eXtensible Markup Language (CHAINXML) and a visual notation were developed for expressing and depicting help models. A technique was also devised for presenting CHAIN help models over legacy applications, whether or not their source code is available. A supporting tool was developed as an extension to Cedar Studio.

Source code can be analyzed to understand how software developers apply certain principles. The results of such analysis show whether software developers are using certain principles or programming language features. As a starting point in this research, I developed a software called CARE#. The latter can analyze the source code of C# software programs to determine how software developers are using and misusing implicit and explicit typing. CARE# can also automatically refactor the source code to improve the developers’ typing choices in terms of consistency and readability.

Cedar is a platform targeting the development of adaptive user interfaces for enterprise applications, using a model-driven approach. Cedar’s primary aim is user interface simplification, which comprises role-based feature-set minimization and layout optimization. Enterprise software applications include many scenarios, where end-users with different roles require variable versions of the same user interface. Catering to this variability, by providing multiple user interface versions, would enhance usability. This research contributed: a reference architecture (Cedar Architecture), an adaptation technique (RBUIS), and a supporting IDE (Cedar Studio).

With the constant evolution of technology and increase in business process complexity, ERP systems had to drastically evolve to accommodate the needs of modern businesses. This drives the implementation of such systems to become very complex hence creating a high risk of implementation failure. This work mainly aims to establish a systematic framework that helps reduce the risk of ERP implementation failures to protect businesses from possible financial losses.

The term spatial data signifies all geometric objects of different dimensions and the relations that could bind these objects together. Crisp spatial data has determinate coordinates and boundaries, whereas vague spatial data has indeterminate ones. The main goal of this research is to analyze spatial data in detail and to propose a method for storing and manipulating such data. To prove our concepts, we designed and implemented a spatial extension package (SPEX) and integrated it into a commercial relational database management system. We also devised the necessary visual components and a software prototype to test our package.