Author Type

Graduate Student

Date of Award

2001

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Khoshgoftaar, Taghi M.

Abstract

Software quality models are tools for detecting faults early in the software development process. In this research, the TREEDISC algorithm and a general classification rule were used to create classification tree models and predict software quality by classifying software modules as fault-prone or not fault-prone. Software metrics were collected from four consecutive releases of a very large legacy telecommunications system with six subsystems. Using release 1, four classification tree models were built using raw metrics, and another four tree models were built using PCA metrics. Models were then selected based on release 2. Releases 3 and 4 were used to validate the selected model. Models that used PCA metrics were as good as or better than models that used raw metrics. This study also investigated the performance of classification tree models, when the subsystem identifier was included as a predictor.

Extent

103 p.

Comments

College of Engineering and Computer Science|FAU Electronic Theses and Dissertations Collection|Thesis (M.S.)--Florida Atlantic University, 2001.|College of Engineering and Computer Science

Identifier

FADT12747

Format

pdf

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