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empirical study of source code complexity and source code modifications during testing and maintenance

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Date Issued:
1994
Summary:
Since maintenance is the most expensive phase of the software life cycle, detecting most of the errors as early as possible in the software development effort can provide substantial savings. This study investigates the behavior of complexity metrics during testing and maintenance, and their relationship to modifications made to the software. Interface complexity causes most of the change activities during integration testing and maintenance, while size causes most of the changes during unit testing. Principal component analysis groups 16 complexity metrics into four domains. Changes in domain pattern are observed throughout the software life cycle. Using those domains as input, regression analysis shows that software complexity measures collected as early as the unit testing phase can identify and predict change prone modules. With a low rate of misclassification, discriminant analysis further confirms that complexity metrics provide a strong indication of the changes made to a module during testing and maintenance.
Title: An empirical study of source code complexity and source code modifications during testing and maintenance.
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Name(s): De Gramont, Anne H.
Florida Atlantic University, Degree grantor
Khoshgoftaar, Taghi M., Thesis advisor
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1994
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 71 p.
Language(s): English
Summary: Since maintenance is the most expensive phase of the software life cycle, detecting most of the errors as early as possible in the software development effort can provide substantial savings. This study investigates the behavior of complexity metrics during testing and maintenance, and their relationship to modifications made to the software. Interface complexity causes most of the change activities during integration testing and maintenance, while size causes most of the changes during unit testing. Principal component analysis groups 16 complexity metrics into four domains. Changes in domain pattern are observed throughout the software life cycle. Using those domains as input, regression analysis shows that software complexity measures collected as early as the unit testing phase can identify and predict change prone modules. With a low rate of misclassification, discriminant analysis further confirms that complexity metrics provide a strong indication of the changes made to a module during testing and maintenance.
Identifier: 15089 (digitool), FADT15089 (IID), fau:11867 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.C.S.)--Florida Atlantic University, 1994.
Subject(s): Computer software--Development
Software maintenance
Source code (Computer science)
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/15089
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.