Web development productivity improvement through object-oriented application framework

Most of the commercial and industrial web applications are complex, difficult to implement, risky to maintain and requires deep understanding of the requirements for customization. As today's software market is more competitive, productivity has become a major concern in software development industry. The aim of this research is to design and develop an application framework for accelerating web development productivity through object-oriented technology. It allows customization, design reuse and automatic code generation to support productivity improvement as a breakthrough solution for the given problem. This research employed systematic literature review (SLR) to identify the source of complexity and productivity factors. Agile development methodology was used to design the framework and it was validated by empirical data from two commercial projects. Results showed that object-oriented application framework (OOAF) has significant factors that affect productivity and dramatically improve higher productivity over traditional approach. It has fulfilled the current needs by reducing complexities, development efforts and accelerates web development productivity. This research contributes in the area of software engineering, specifically in the field of software productivity improvement and software customization. These will lead to faster development time for software industries

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

Using guidelines to improve quality in software nonfunctional attributes

Software development aims to produce software systems that satisfy two requirement categories: functional and quality. One aspect of software quality is nonfunctional attributes (NFAs), such as security, performance, and availability. Software engineers can meet NFA requirements by applying suitable guidelines during software development. However, this process is complicated by the different effects of different guidelines on NFA quality and the relationships among the guidelines themselves. Thus, finding a suitable set of guidelines is not straightforward. This article introduces a step-by-step approach that gives software engineers a suitable guideline set to apply to improve NFA quality during the software development life cycle. The approach manages the effects different guidelines have on both the attributes and the relationships among the guidelines