Issuing Body

Mississippi State University

Advisor

Allen, Edward B.

Committee Member

Boggess, Julian E.

Committee Member

Bridges, Susan M.

Date of Degree

1-1-2004

Document Type

Graduate Thesis - Open Access

Major

Computer Science

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Computer Science and Engineering

Abstract

Developing high quality software is the goal of every software development organization. Software quality models are commonly used to assess and improve the software quality. These models, based on the past releases of the system, can be used to identify the fault-prone modules for the next release. This information is useful to the open-source software community, including both developers and users. Developers can use this information to clean or rebuild the faulty modules thus enhancing the system. The users of the software system can make informed decisions about the quality of the product. This thesis builds quality models using logistic regression, neural networks, decision trees, and genetic algorithms and compares their performance. Our results show that an overall accuracy of 65 ? 85% is achieved with a type II misclassification rate of approximately 20 ? 35%. Performance of each of the methods is comparable to the others with minor variations.

URI

https://hdl.handle.net/11668/19177

Comments

software quality modeling||machine learning||logistic regression||principal components analysis||c4.5||nn||ga

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