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

In today's competitive environment for software products, quality has become an increasingly important asset to software development organizations. Software quality models are tools for focusing efforts to find faults early in the development. Delaying corrections can lead to higher costs. In this research, the classification tree modeling technique was used to predict the software quality by classifying program modules either as fault-prone or not fault-prone. The S-Plus regression tree algorithm and a general classification rule were applied to yield classification tree models. Two classification tree models were developed based on four consecutive releases of a very large legacy telecommunications system. The first release was used as the training data set and the subsequent three releases were used as evaluation data sets. The first model used twenty-four product metrics and four execution metrics as candidate predictors. The second model added fourteen process metrics as candidate predictors.

Extent

93 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

FADT15707

Format

pdf

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