You are here

comprehensive comparative study of multiple classification techniques for software quality estimation

Download pdf | Full Screen View

Date Issued:
2003
Summary:
Reliability and quality are desired features in industrial software applications. In some cases, they are absolutely essential. When faced with limited resources, software project managers will need to allocate such resources to the most fault prone areas. The ability to accurately classify a software module as fault-prone or not fault-prone enables the manager to make an informed resource allocation decision. An accurate quality classification avoids wasting resources on modules that are not fault-prone. It also avoids missing the opportunity to correct faults relatively early in the development cycle, when they are less costly. This thesis seeks to introduce the classification algorithms (classifiers) that are implemented in the WEKA software tool. WEKA (Waikato Environment for Knowledge Analysis) was developed at the University of Waikato in New Zealand. An empirical investigation is performed using a case study at a real-world system.
Title: A comprehensive comparative study of multiple classification techniques for software quality estimation.
0 views
0 downloads
Name(s): Puppala, Kishore.
Florida Atlantic University, Degree grantor
Khoshgoftaar, Taghi M., Thesis advisor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 2003
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 136 p.
Language(s): English
Summary: Reliability and quality are desired features in industrial software applications. In some cases, they are absolutely essential. When faced with limited resources, software project managers will need to allocate such resources to the most fault prone areas. The ability to accurately classify a software module as fault-prone or not fault-prone enables the manager to make an informed resource allocation decision. An accurate quality classification avoids wasting resources on modules that are not fault-prone. It also avoids missing the opportunity to correct faults relatively early in the development cycle, when they are less costly. This thesis seeks to introduce the classification algorithms (classifiers) that are implemented in the WEKA software tool. WEKA (Waikato Environment for Knowledge Analysis) was developed at the University of Waikato in New Zealand. An empirical investigation is performed using a case study at a real-world system.
Identifier: 9780496198887 (isbn), 13039 (digitool), FADT13039 (IID), fau:9904 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.)--Florida Atlantic University, 2003.
Subject(s): Software engineering
Computer software--Quality control
Decision trees
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/13039
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.