Managing software evolution through midleware and policy-based software adaptation framework

Software evolution is a process that is needed in order for software to remain useful. Thus, software evolution should be properly planned and controlled to prevent its negative impact from affecting any organization. Software adaptation concept is one of the promising ways to control software evolution. In this approach, software is made adaptable to minimize the impact of change. A lot of researches on software adaptation focus on adaptability of mobile based and network application due to its context sensitivity and quality-of-service requirements. However, there is still lack of work in enterprise system domain with multiple delivery channels, which focus on adaptability of its context environment such as the changes introduced to its devices. Hence, the purpose of this research is to develop a middleware and policy-based, adaptation framework to manage negative effects of software evolution in an enterprise system. The main research focus is on the changes introduced at the device layer. The concept of policy is used to specify adaptations requirements. This research provides a framework called Middleware and Policy-Based Framework to Manage Software Evolution (MiPAF), which can be used to develop adaptive software, allowing parameterized and compositional adaptation. Furthermore, the framework can be used by client-server and web-based application. A policy language called MiPAF Policy Language (MPL) is created to be used with the framework. MiPAF is formally specified using Z Notation and the policy language is described using pseudo code. A tool is provided to assist developers in creating the policy. For evaluation of the framework, a set of runtime components were developed and implemented for Unit Trust System (UTS) Front-end and web-based UTS, two industrial-based case studies. The evaluation result shows that MiPAF excellently fulfil all the evaluation criteria described in this thesis

A Framework for Improving Adaptive Data Visualization in Decision Support Systems

Adaptive approaches are used to improve user experience and satisfaction for web browsing, based on profiling information gathered from user interactions. In decision support systems, the need for personalization adaptation has increased in order to provide more immediate and relevant information to decision makers, using web based access to data. Using visualizations for rendering complex query results, in real-time is of particular importance in many application domains. In this thesis we propose an approach, and a framework, for measuring history, experiences and satisfaction of users of a healthcare decision support system. The focus is on user selections of visualizations, based on the nature of queries generated. The aim of this framework is intended to provide collection of individual user experiences and satisfaction, in order to obtain a user population profile for later studies. The model used is a weighting scheme, but is designed to support later extensions and enhancements using \u27AI reasoning techniques\u27. This model was implemented and a usability study was conducted to validate improvements compared to non adaptive data visualization systems. The outcome of this research may lead to increased accuracy and reduced time of selection of visualization, over repeated usage, and is therefore important as a productivity enhancement approach