Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Software defect prediction using maximal information coefficient and fast correlation-based filter feature selection

Software quality ensures that applications that are developed are failure free. Some modern systems are intricate, due to the complexity of their information processes. Software fault prediction is an important quality assurance activity, since it is a mechanism that correctly predicts the defect proneness of modules and classifies modules that saves resources, time and developers’ efforts. In this study, a model that selects relevant features that can be used in defect prediction was proposed. The literature was reviewed and it revealed that process metrics are better predictors of defects in version systems and are based on historic source code over time. These metrics are extracted from the source-code module and include, for example, the number of additions and deletions from the source code, the number of distinct committers and the number of modified lines. In this research, defect prediction was conducted using open source software (OSS) of software product line(s) (SPL), hence process metrics were chosen. Data sets that are used in defect prediction may contain non-significant and redundant attributes that may affect the accuracy of machine-learning algorithms. In order to improve the prediction accuracy of classification models, features that are significant in the defect prediction process are utilised. In machine learning, feature selection techniques are applied in the identification of the relevant data. Feature selection is a pre-processing step that helps to reduce the dimensionality of data in machine learning. Feature selection techniques include information theoretic methods that are based on the entropy concept. This study experimented the efficiency of the feature selection techniques. It was realised that software defect prediction using significant attributes improves the prediction accuracy. A novel MICFastCR model, which is based on the Maximal Information Coefficient (MIC) was developed to select significant attributes and Fast Correlation Based Filter (FCBF) to eliminate redundant attributes. Machine learning algorithms were then run to predict software defects. The MICFastCR achieved the highest prediction accuracy as reported by various performance measures.School of ComputingPh. D. (Computer Science

An Information Security Policy Compliance Reinforcement and Assessment Framework

The majority of SMEs have adopted the use of information communication and technology (ICT) services. However, this has exposed their systems to new internal and external security vulnerabilities. These SMEs seem more concerned with external threat related vulnerabilities rather than those from internal threats, although researchers and industry are suggesting a substantial proportion of security incidents to be originating from insiders. Internal threat is often addressed by, firstly, a security policy in order to direct activities and, secondly, organisational information security training and awareness programmes. These two approaches aim to ensure that employees are proficient in their roles and that they know how to carry out their responsibilities securely. There has been a significant amount of research conducted to ensure that information security programmes communicate the information security policy effectively and reinforce sound security practice. However, an assessment of the genuine effectiveness of such programmes is seldom carried out. The purposes of this research study were, firstly, to highlight the flaws in assessing behavioural intentions and equating such behavioural intentions with actual behaviours in information security; secondly, to present an information security policy compliance reinforcement and assessment framework which assists in promoting the conversion of intentions into actual behaviours and in assessing the behavioural change. The approach used was based on the Theory of Planned Behaviour, knowledge, attitude and behaviour theory and Deterrence Theory. Expert review and action research methods were used to validate and refine the framework. The action research was rigorously conducted in four iterations at an SME in South Africa and involved 30 participating employees. The main findings of the study revealed that even though employees may have been well trained and are aware of information security good practice, they may be either unable or unwilling to comply with such practice. The findings of the study also revealed that awareness drives which lead to secure behavioural intents are merely a first step in information security compliance. The study found that not all behavioural intentions converted to actual secure behaviours and only 64% converted. However, deterrence using rewards for good behaviour and punishment for undesirable behaviour was able to increase the conversion by 21%