Date of Award

Fall 2004

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Computing Sciences - (Ph.D.)

Department

Computer Science

First Advisor

Ali Mili

Second Advisor

Joseph Y-T. Leung

Third Advisor

Dimitri Theodoratos

Fourth Advisor

Vincent Oria

Fifth Advisor

Fu Li

Abstract

Examining the development and trends in software engineering technology is a huge undertaking. It is constantly evolving and affected by a large number of factors, which are themselves driven by a wide range of sub-factors. This dissertation is part of a long term project intended to analyze software engineering technology trends and how they evolve. This project is intended to analyze operating system trends and what are the factors that drive how they evolve. Basically, the following questions will be answered: "How to watch, predict, adapt to, and affect operating system's evolution trends?"

In previous research, YF Chen used statistical models to analyze the evolution of programming languages. Building upon Chen's work, the author uses operating systems as the subject, derives the statistical models and applies them to analyze the trend and the relationships between different factors that characterize an operating system.

After the history of several operating systems is reviewed, it shows that two kinds of factors, intrinsic factors and extrinsic factors, could affect the evolution of an operating system. Intrinsic factors are used to describe the general design criteria of an operating system. On the other hand, extrinsic factors are the factors that are not directly related to the general attributes of an operating system. In order to describe the relationship of these factors and how they affect operating system trends, they need to be quantified. For intrinsic factors, data are collected from different trustable data sources and analyzed. For extrinsic factors, historical data are collected and established as a data warehouse. The operating system trends are described and evaluated by using all the data that have been collected and analyzed.

In this dissertation, statistical methods are used to describe historical operating system trends and predict the future trends. Several statistics models are constructed to describe the relationships among these factors. Canonical correlation is used to do the factor analysis. Multivariate multiple regression method has been used to construct the statistics models for the evolution of operating system trends. The models are validated by comparing the predicted data with the actual data.

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