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

Optimising the usability of content rich e-learning material: an eye tracking experiment

This research was aimed at the optimisation of the usability of content-rich computer and mobile based e-learning material. The goal was to preserve the advantages of paper based material in designing optimised modules that were mobile and computer-based, but at the same time avoiding the pitfalls of converting traditional paper based learning material for use on screen. A mobile eye tracker was used to analyse how students studied similar course content on paper, and on mobile device. Screen based eye tracking was also used to analyse how participants studied corresponding content on a desktop screen. Eye movements which were recorded by an eye tracker revealed the sequences of fixations and saccades on the text that was read by each participant. By analysing and comparing the eye gaze patterns of students reading the same content on three different delivery platforms, the differences between these platforms were identified in terms of their delivery of content rich, text based study material. The results showed that more students read online content on a computer screen than on mobile devices. The inferential analysis revealed that the differences in reading duration, comprehension, linearity and fixation count on the three platforms were insignificant. There were significant differences in saccade length. This analysis was used to identify strong aspects of the respective platforms and consequently derive guidelines for using these aspects optimally to design content rich material for delivery on computer screen and mobile device. The limitations of each platform were revealed and guidelines for avoiding these were derivedComputingM. Sc. (Computing