Author Type

Graduate Student

Date of Award

2002

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Khoshgoftaar, Taghi M.

Abstract

The increased reliance on computer systems in the modern world has created a need for engineering reliability control of computer systems to the highest standards. Software quality classification models are one of the important tools to achieve high reliability. They can be used to calibrate software metrics-based models to predict whether software modules are fault-prone or not. Timely use of such models can aid in detecting faults early in the life cycle. Individual classifiers may be improved by using the combined decision from multiple classifiers. Several algorithms implement this concept and are investigated in this thesis. These combined learners provide the software quality modeling community with accurate, robust, and goal oriented models. This study presents a comprehensive comparative evaluation of meta learners using a strong and a weak learner, C4.5 and Decision Stump, respectively. Two case studies of industrial software systems are used in our empirical investigations.

Extent

220 p.

Comments

College of Engineering and Computer Science|FAU Electronic Theses and Dissertations Collection|Thesis (M.S.)--Florida Atlantic University, 2002.|College of Engineering and Computer Science

Identifier

FADT12898

Format

pdf

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