Author Type

Graduate Student

Date of Award

1999

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Khoshgoftaar, Taghi M.

Abstract

Software quality is crucial both to software makers and customers. However, in reality, improvement of quality and reduction of costs are often at odds. Software modeling can help us to detect fault-prone software modules based on software metrics, so that we can focus our limited resources on fewer modules and lower the cost but still achieve high quality. In the present study, a tree classification modeling technique---TREEDISC was applied to three case studies. Several major contributions have been made. First, preprocessing of raw data was adopted to solve the computer memory problem and improve the models. Secondly, TREEDISC was thoroughly explored by examining the roles of important parameters in modeling. Thirdly, a generalized classification rule was introduced to balance misclassification rates and decrease type II error, which is considered more costly than type I error. Fourthly, certainty of classification was addressed. Fifthly, TREEDISC modeling was validated over multiple releases of software product.

Extent

86 p.

Comments

College of Engineering and Computer Science|FAU Electronic Theses and Dissertations Collection|Thesis (M.S.)--Florida Atlantic University, 1999.|College of Engineering and Computer Science

Identifier

FADT15718

Format

pdf

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