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In two studies using computational cognitive modelling techniques, two large websites were analysed and then their information architecture was improved. In each study, a psychological experiment was designed and executed comparing the improved version of the website with the original version. The findings demonstrate that users' information-retrieval performance of sites with improved information architecture is superior.

In our research, we focus on these three issues, and concentrate on developing a metaheuristic framework for solving the UTRP.  Embedding simple heuristic algorithms (hill-climbing and simulated annealing) within this framework, we have beaten previously best published results for Mandi’s benchmark problem, which is the only generally available data set.  Due to the lack of “standard models” for the UTRP, and a shortage of benchmark data it is difficult for researchers to compare their approaches.  Thus we introduce a simplified model and implement a data set generation program to produce realistic test data sets much larger than Mandi’s problem.  Furthermore, some Lower Bounds and necessary constraints of the UTRP are also researched, which we use to help validate the quality of our results, particularly those obtained for our new data sets. Finally, a multi-objective optimisation algorithm is designed to solve our urban transit routing problem in which the operator’s cost is modelled in addition to passenger quality of service.

Building models of high-dimensional data in a low dimensional space has become extremely popular in recent years. Motion tracking, facial animation, stock market tracking, digital libraries and many other different models have been built and tuned to specific application domains. However, when the underlying structure of the original data is unknown, the modelling of such data is still an open question. The problem is of interest as capturing and storing large amounts of high dimensional data has become trivial, yet the capability to process, interpret, and use this data is limited. In this thesis, we introduce novel algorithms for modelling high dimensional data with an unknown structure, which allows us to represent the data with good accuracy and in a compact manner. This work presents a novel fully automated dynamic hierarchical algorithm, together with a novel automatic data partitioning method to work alongside existing specific models (talking head, human motion). Our algorithm is applicable to hierarchical data visualisation and classification, meaningful pattern extraction and recognition, and new data sequence generation. Also during our work we investigated problems related to low dimensional data representation: automatic optimal input parameter estimation, and robustness against noise and outliers. We show the potential of our modelling with many data domains: talking head, motion, audio, etc. and we believe that it has good potential in adapting to other domains.

With the continuous advancement in hardware technologies, significant research has been devoted to design and develop high-level parallel programming models that allow programmers to exploit the latest developments in heterogeneous multi-core/many-core architectures. Structural programming paradigms propose a viable solution for e ciently programming modern heterogeneous multi-core architectures equipped with one or more programmable Graphics Processing Units (GPUs). Applying structured programming paradigms, it is possible to subdivide a system into building blocks (modules, skids or components) that can be independently created and then used in di erent systems to derive multiple functionalities. Exploiting such systematic divisions, it is possible to address extra-functional features such as application performance, portability and resource utilisations from the component level in heterogeneous multi-core architecture. While the computing function of a building block can vary for di erent applications, the behaviour (semantic) of the block remains intact. Therefore, by understanding the behaviour of building blocks and their structural compositions in parallel patterns, the process of constructing and coordinating a structured application can be automated. In this thesis we have proposed Structural Composition and Interaction Protocol (SKIP) as a systematic methodology to exploit the structural programming paradigm (Building block approach in this case) for constructing a structured application and extracting/injecting information from/to the structured application. Using SKIP methodology, we have designed and developed Performance Enhancement Infrastructure (PEI) as a SKIP compliant autonomic behavioural framework to automatically coordinate structured parallel applications based on the extracted extra-functional properties related to the parallel computation patterns. We have used 15 di erent PEI-based applications (from large scale applications with heavy input workload that take hours to execute to small-scale applications which take seconds to execute) to evaluate PEI in terms of overhead and performance improvements. The experiments have been carried out on 3 di erent Heterogeneous (CPU/GPU) multi-core architectures (including one cluster machine with 4 symmetric nodes with one GPU per node and 2 single machines with one GPU per machine). Our results demonstrate that with less than 3% overhead, we can achieve up to one order of magnitude speed-up when using PEI for enhancing application performance.

In software engineering environments, motivation has become an imperative tool for increasing the productivity and creativity levels of projects. The aim of this research is to develop a validated conceptual multifactor and motivating model that represents the interaction between the organisational, occupational and interpersonal factors in software engineering environments. However, the application of well-known motivation tools cannot guarantee high motivational levels among the members of software engineering teams. Therefore, several phenomena have been monitored and empirically tested related to the daily practices in the software engineering industry. Reviewing the literature on motivation in software engineering uncovered a list of influential factors that could motivate individuals in the workplace. These factors have been suggested as being grouped into three categories (interpersonal, occupational and organisational). The literature review stage was followed by a preliminary study to discuss and validate these factors in greater detail by interviewing eight experts drawn from the software engineering industry. The preliminary study provided this research with an initial conceptual model that could broaden the understanding of the recent state of motivation in software engineering environments. The initial model was validated and expanded by conducting two types of research (quantitative and qualitative) based on the type of information gleaned. Accordingly, 208 experienced software engineers and members of teams in the software development industry were involved in this research. The results from this research revealed a statistically significant interaction between factors from different categories (interpersonal, occupational and organisational). This interaction has helped in developing an updated new model of motivation in software engineering. In addition, the application of motivation theories in software engineering could be affected by some work-related factors. These factors were found in this research to be member role, contract types, age, organisational structure and citizenship status. Thus, all these factors have been given a high consideration when designing rewards systems in software engineering.

This work provides a holistic investigation into the realm of feature modeling within software product lines. The work presented identifies limitations and challenges within the current feature modeling approaches. Those limitations include, but not limited to, the dearth of satisfactory cognitive presentation, inconveniency in scalable systems, inflexibility in adapting changes, nonexistence of predictability of models behavior, as well as the lack of probabilistic quantification of model’s implications and decision support for reasoning under uncertainty. The work in this thesis addresses these challenges by proposing a series of solutions. The first solution is the construction of a Bayesian Belief Feature Model, which is a novel modeling approach capable of quantifying the uncertainty measures in model parameters by a means of incorporating probabilistic modeling with a conventional modeling approach. The Bayesian Belief feature model presents a new enhanced feature modeling approach in terms of truth quantification and visual expressiveness. The second solution takes into consideration the unclear support for the reasoning under the uncertainty process, and the challenging constraint satisfaction problem in software product lines. This has been done through the development of a mathematical reasoner, which was designed to satisfy the model constraints by considering probability weight for all involved parameters and quantify the actual implications of the problem constraints. The developed Uncertain Constraint Satisfaction Problem approach has been tested and validated through a set of designated experiments. Profoundly stating, the main contributions of this thesis include the following: • Develop a framework for probabilistic graphical modeling to build the purported Bayesian belief feature model. • Extend the model to enhance visual expressiveness throughout the integration of colour degree variation; in which the colour varies with respect to the predefined probabilistic weights. • Enhance the constraints satisfaction problem by the uncertainty measuring of the parameters truth assumption. • Validate the developed approach against different experimental settings to determine its functionality and performance.

Software Architecture is a high level description of a software intensive system that enables architects to have a better intellectual control over the complete system. It is also used as a communication vehicle among the various system stakeholders. Variability in software-intensive systems is the ability of a software artefact (e.g., a system, subsystem, or component) to be extended, customised, or configured for deployment in a specific context. Although variability in software architecture is recognised as a challenge in multiple domains, there has been no formal consensus on how variability should be captured or represented. In this research, we addressed the problem of representing variability in software architecture through a three phase approach. First, we examined existing literature using the Systematic Literature Review (SLR) methodology, which helped us identify the gaps and challenges within the current body of knowledge. Equipped with the findings from the SLR, a set of design principles have been formulated that are used to introduce variability management capabilities to an existing Architecture Description Language (ADL). The chosen ADL was developed within our research group (ALI) and to which we have had complete access. Finally, we evaluated the new version of the ADL produced using two distinct case studies: one from the Information Systems domain, an Asset Management System (AMS); and another from the embedded systems domain, a Wheel Brake System (WBS). This thesis presents the main findings from the three phases of the research work, including a comprehensive study of the state-of-the-art; the complete specification of an ADL that is focused on managing variability; and the lessons learnt from the evaluation work of two distinct real-life case studies.

Variability management is one of the major challenges in software product line adoption, since it needs to be efficiently managed at various levels of the software product line development process (e.g., requirement analysis, design, implementation, etc.). One of the main challenges within variability management is the handling and effective visualization of large-scale (industry-size) models, which in many projects, can reach the order of thousands, along with the dependency relationships that exist among them. These have raised many concerns regarding the scalability of current variability management tools and techniques and their lack of industrial adoption. To address the scalability issues, this work employed a combination of quantitative and qualitative research methods to identify the reasons behind the limited scalability of existing variability management tools and techniques. In addition to producing a comprehensive catalogue of existing tools, the outcome form this stage helped understand the major limitations of existing tools. Based on the findings, a novel approach was created for managing variability that employed two main principles for supporting scalability. First, the separation-of-concerns principle was employed by creating multiple views of variability models to alleviate information overload. Second, hyperbolic trees were used to visualise models (compared to Euclidian space trees traditionally used). The result was an approach that can represent models encompassing hundreds of variability points and complex relationships. These concepts were demonstrated by implementing them in an existing variability management tool and using it to model a real-life product line with over a thousand variability points. Finally, in order to assess the work, an evaluation framework was designed based on various established usability assessment best practices and standards. The framework was then used with several case studies to benchmark the performance of this work against other existing tools.

Since mid-1990s, companies have adopted agile methods and incorporated them in their development methodologies. For this reason, future project managers and developers need to have a full understanding of these methods. At present, the university’s approach to agile methods is theoretical and is not reflected during the development of a product and their practical use. The purpose of this project is the creation of a software system in the form of a game, named Agile Game, which simulates their use. The system is designed for use as supplementary material in lectures, to help students understand agile methods, to present their use within a project, and to demonstrate how they differ from traditional project management methodologies. The final system, which is web based, was implemented using PHP, MySQL and JavaScript. It was fully tested against the requirements and evaluated by peer students. The evaluation showed that the majority of users were satisfied with the system but they thought that it should contain more detailed information at every step of the game. For this reason, some parts of the design and the content were reviewed to meet user requirements.