SAGA: A project to automate the management of software production systems

The Software Automation, Generation and Administration (SAGA) project is investigating the design and construction of practical software engineering environments for developing and maintaining aerospace systems and applications software. The research includes the practical organization of the software lifecycle, configuration management, software requirements specifications, executable specifications, design methodologies, programming, verification, validation and testing, version control, maintenance, the reuse of software, software libraries, documentation, and automated management

Software process modelling as relationships between tasks

Systematic formulation of software process models is currently a challenging problem in software engineering. We present an approach to define models covering the phases of specification, design, implementation and testing of software systems in the component programming framework, taking into account non-functional aspects of software (efficiency, etc.), automatic reusability of implementations in systems and also prototyping techniques involving both specifications and implementations. Our proposal relies on the identification of a catalogue of tasks that appear during these phases which satisfy some relationships concerning their order of execution. A software process model can be defined as the addition of more relationships over these tasks using a simple, modular process language. We have developed also a formal definition of correctness of a software development with respect to a software process model, based on the formulation of models as graphs.Peer ReviewedPostprint (published version

Using formal methods to support testing

Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing

Weather corrected electricity demand forecasting

Electricity load forecasts now form an essential part of the routine operations of electricity companies. The complexity of the short-term load forecasting (STLF) problem arises from the multiple seasonal components, the change in consumer behaviour during holiday seasons and other social and religious events that affect electricity consumption. The aim of this research is to produce models for electricity demand that can be used to further the understanding of the dynamics of electricity consumption in South Wales. These models can also be used to produce weather corrected forecasts, and to provide short-term load forecasts. Two novel time series modelling approaches were introduced and developed. Profiles ARIMA (PARIMA) and the Variability Decomposition Method (VDM). PARIMA is a univariate modelling approach that is based on the hierarchical modelling of the different components of the electricity demand series as deterministic profiles, and modelling the remainder stochastic component as ARIMA, serving as a simple yet versatile signal extraction procedure and as a powerful prewhitening technique. The VDM is a robust transfer function modelling approach that is based on decomposing the variability in time series data to that of inherent and external. It focuses the transfer function model building on explaining the external variability of the data and produces models with parameters that are pertinent to the components of the series. Several candidate input variables for the VDM models for electricity demand were investigated, and a novel collective measure of temperature the Fair Temperature Value (FTV) was introduced. The FTV takes into account the changes in variance of the daily maximum and minimum temperatures with time, making it a more suitable explanatory variable for the VDM model. The novel PARIMA and VDM approaches were used to model the quarterly, monthly, weekly, and daily demand series. Both approaches succeeded where existing approaches were unsuccessful and, where comparisons are possible, produced models that were superior in performance. The VDM model with the FTV as its explanatory variable was the best performing model in the analysis and was used for weather correction. Here, weather corrected forecasts were produced using the weather sensitive components of the PARIMA models and the FTV transfer function component of the VDM model

Z and high level Petri nets

High level Petri nets have tokens with values, traditionally called colors, and transitions that produce tokens in a functional way, using the consumed tokens as arguments of the function application. Large nets should be designed in a topdown approach and therefore we introduce a hierarchical net model which combines a data flow diagram technique with a high level Petri net model. We use Z to specify this net model, which is in fact the metamodel for specific systems. Specific models we specify partly by diagrams and partly in Z. We give some advantages and disadvantages of using Z in this way. Finally we show how to specify systems by means of an example