Software development management using metamodels and activity networks

This thesis develops the concept, management and control of metamodels for the management of software development projects. Metamodels provide a more flexible approach for managing and controlling the software engineering process and are based on the integration of several software development paradigms. Generalised Activity Networks are used to provide the more powerful planning techniques required for managing metamodels. In this thesis, both new node logics, that clarify previous work in this field, and Generalised Activity-on-the-Arrow and Generalised Activity-on-the-Node representations are developed and defined. Activity-on-the-Node representations reflect the current mood of the project management industry and allow constraints to be applied directly to logical dependencies between activities. The Generalised Activity Networks defined within this thesis can be used as tools to manage risks and uncertainties in both software developments and general engineering projects. They reflect the variation and uncertainties in projects more realistically and improve the planning and scheduling of such projects. [Continues.

The effect of a computer-based cartooning tool on children’s cartoons and written stories

This paper reports a study assessing a new computer tool for cartoon storytelling, created by the authors for a target audience in the upper half of the English and Welsh Key Stage 2 (Years 5 and 6, covering ages 9 to 11 years). The tool attempts to provide users with more opportunities for expressive visualisation than previous educational software; its design was motivated by earlier work connecting “moving image literacy” with print literacy, and it was used here in storywriting preparation work: users first visualised a known story, then wrote their versions of it based on the cartoons they had made. The stories produced are compared with stories written using two other preparation activities, one a pencil-and-paper cartooning exercise and the other a teacher’s normal planning session, which also resulted in a retelling of a known story. The study finds that no one preparation process had a noticeably different effect on the final written stories; however, the cartoons produced with the software are found to be quite different to their paper counterparts, showing a greater variety of character action, pose and interaction, slightly more variety of camera shot distance, and more pictures. Children’s and teachers’ reactions to the software tool are also discussed

Cartoons beyond clipart: a computer tool for storyboarding and storywriting

This paper describes the motivation, proposal, and early prototype testing of a computer tool for story visualisation prior to storywriting. An analysis of current software for making various types of visual story is made; this identifies a gap between software which emphasises preset banks of artwork, and software which emphasises low-level construction and/or drawing. A proposal is made to fill this gap, and a prototype implementation of the proposal is described in the context of a school-based study with Year 5, covering ages 9-10 years. Results from this prototype study both validate the novel proposal made and demonstrate that children are capable of more complex graphical interaction than most current software permits

Mimicking human strategies in fighting games using a data driven finite state machine

Multiplayer fighting videogames have become an increasingly popular over the last few years, especially with the introduction of online play, making for a more competitive experience. Multiplayer fighting games give players the opportunity to utilize particular strategies and tactics to win, allowing them to use their own signature style. As a player can only play against a particular opponent who is actively participating in the game themselves, they cannot practice combating the opponent’s style if the opponent is not participating in the game. This paper presents a novel approach for an avatar to learn and mimic the style of a player. It does this by recording and analyzing the data before splitting it up into two tiers; tactical data and strategic data.. The approach uses a Naïve Bayes classifier to classify the tactics to particular states, and a Data Driven Finite State Machine to dictate when certain tactics are used. Statistics recorded during an experiment involving the approach are discussed, which indicate that the architecture of the Artificial Intelligence is fit for purpose, but does require refinement. Limitations of the architecture are discussed, including that such an approach may not provide accurate results when more parameters are considered

HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts

This paper presents details of an open access web site that can be used by hydrologists and other scientists to evaluate time series models. There is at present a general lack of consistency in the way in which hydrological models are assessed that handicaps the comparison of reported studies and hinders the development of superior models. The HydroTest web site provides a wide range of objective metrics and consistent tests of model performance to assess forecasting skill. This resource is designed to promote future transparency and consistency between reported models and includes an open forum that is intended to encourage further discussion and debate on the topic of hydrological performance evaluation metrics. It is envisaged that the provision of such facilities will lead to the creation of superior forecasting metrics and the development of international benchmark time series datasets

Flood estimation at ungauged sites using artificial neural networks

Artificial neural networks (ANNs) have been applied within the field of hydrological modelling for over a decade but relatively little attention has been paid to the use of these tools for flood estimation in ungauged catchments. This paper uses data from the Centre for Ecology and Hydrology's Flood Estimation Handbook (FEH) to predict T-year flood events and the index flood (the median of the annual maximum series) for 850 catchments across the UK. When compared with multiple regression models, ANNs provide improved flood estimates that can be used by engineers and hydrologists. Comparisons are also made with the empirical model presented in the FEH and a preliminary study is made of the spatial distribution of ANN residuals, highlighting the influence that geographical factors have on model performance

Ideal point error for model assessment in data-driven river flow forecasting

When analysing the performance of hydrological models in river forecasting, researchers use a number of diverse statistics. Although some statistics appear to be used more regularly in such analyses than others, there is a distinct lack of consistency in evaluation, making studies undertaken by different authors or performed at different locations difficult to compare in a meaningful manner. Moreover, even within individual reported case studies, substantial contradictions are found to occur between one measure of performance and another. In this paper we examine the ideal point error (IPE) metric – a recently introduced measure of model performance that integrates a number of recognised metrics in a logical way. Having a single, integrated measure of performance is appealing as it should permit more straightforward model inter-comparisons. However, this is reliant on a transferrable standardisation of the individual metrics that are combined to form the IPE. This paper examines one potential option for standardisation: the use of naive model benchmarking

Special issue on ‘Computer Science in Sport’

Computer Science in Sport is a cross-disciplinary topic that brings together the problem-solving capabilities of Computer Science to various theoretical and practical aspects of all sports and physical activities [...

Sensitivity analysis of radial basis function networks for river stage forecasting

Sensitivity analysis of neural networks to input variation is an important research area as it goes some way to addressing the criticisms of their black-box behaviour. Such analysis of RBFNs for hydrological modelling has previously been limited to exploring perturbations to both inputs and connecting weights. In this paper, the backward chaining rule that has been used for sensitivity analysis of MLPs, is applied to RBFNs and it is shown how such analysis can provide insight into physical relationships. A trigonometric example is first presented to show the effectiveness and accuracy of this approach for first order derivatives alongside a comparison of the results with an equivalent MLP. The paper presents a real-world application in the modelling of river stage shows the importance of such approaches helping to justify and select such models

Software development process models: a technique for evaluation and decision-making

Process models are the bedrock on which all software development projects are based. Since the first process model was defined in the late 1950s, more contemporary processes have evolved to deal with even more complex projects in even more dynamic problem domains. Although there are many such process models now available for software engineers to follow, they can be classified according to one of five basic types. They differ in the level to which the process might be applied and also in the additional guidelines and philosophies they define. In this paper we define those five fundamental process types. The paper goes on to present a definitive technique for comparing such process models—the Functionality-Time graph. By combining this technique with a Functionality-Cost/Benefit graph, it helps to identify the key decision points in the software development process with respect to a software system's functionality. Such a hybrid technique helps project managers to recognise the point at which to draw closure on an existing system by showing potential losses if the project continues. By attempting to identify where they lie on the graphs defined in this paper, project managers can determine the consequences of decisions at different stages of the software life cycle. The hybrid graphs also provide an invaluable educational tool that help software engineers understand the development processes they adopt and clarifies the differences between them