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

Ensuring quality and reliability in software is important with its growing use in day to day life. Having an estimate of the number of faults in software modules early in their life cycles will enable software project managers to direct testing efforts in those considered risky and reduce the waste of resources in testing the entire software system. Case-based reasoning, abbreviated CBR, is one of the methods which predicts the number of faults in a software. The scope of this thesis is two-fold. First, it empirically investigates the effects of the different factors on the predictive accuracy of CBR. Experiments were done to compare different similarity functions, solution processes, and maximum number of nearest neighbors. Second, it compares the predictive accuracy of CBR models with multiple linear regression and artificial neural network models. The average absolute error and average relative error are used to determine the model with a high accuracy of prediction.

Extent

90 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

FADT12749

Format

pdf

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