| Though software development has been evolving for over 50 years, the development of computer software systems has largely remained an art. Through the application of measurable and repeatable processes, efforts have been made to slowly transform the software development art into a rigorous engineering discipline. The potential gains are tremendous. Computer software pervades modern society in many forms. For example, the automobile, radio, television, telephone, refrigerator, and still-camera have all been transformed by the introduction of computer based controls. The quality of these everyday products is in part determined by the quality of the computer software running inside them. Therefore, the timely delivery of low-cost and high-quality software to enable these mass market products becomes very important to the long term success of the companies building them.;It is not surprising that managing the number of faults in computer software to competitive levels is a prime focus of the software engineering activity. In support of this activity, many models of software quality have been developed to help control the software development process and ensure that our goals of cost and quality are met on time.;In this study, we focus on the software quality modeling activity. We improve existing static and dynamic methodologies and demonstrate new ones in a coordinated attempt to provide engineering methods applicable to the development of computer software. We will show how the power of separate predictive and classification models of software quality may be combined into one model; introduce a three group fault classification model in the object-oriented paradigm; demonstrate a dynamic modeling methodology of the testing process and show how software product measures and software process measures may be incorporated as input to such a model; demonstrate a relationship between software product measures and the testability of software.;The following methodologies were considered: principal components analysis, multiple regression analysis, Poisson regression analysis, discriminant analysis, time series analysis, and neural networks. Commercial grade software systems are used throughout this dissertation to demonstrate concepts and validate new ideas. As a result, we hope to incrementally advance the state of the software engineering "art". |
