Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes

Empirical validation of software metrics suites to predict fault proneness in object-oriented (OO) components is essential to ensure their practical use in industrial settings. In this paper, we empirically validate three OO metrics suites for their ability to predict software quality in terms of fa...

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Veröffentlicht in:IEEE transactions on software engineering Jg. 33; H. 6; S. 402 - 419
Hauptverfasser: Olague, H.M., Etzkorn, L.H., Gholston, S., Quattlebaum, S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2007
IEEE Computer Society
Schlagworte:
Case studies
Computer industry
Computer programs
Digital Object Identifier
Empirical analysis
Java
Logistics
Mathematical models
Mood
Moods
Object oriented
Object oriented modeling
Object oriented programming
object-oriented metrics
Object-oriented software metrics
Open source software
Programming
Public domain
Regression analysis
Software
Software development
Software engineering
Software maintenance
software maintenance programming
Software metrics
Software quality
software quality metrics
software reuse
Studies
Validation studies
ISSN:0098-5589, 1939-3520
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Zusammenfassung:Empirical validation of software metrics suites to predict fault proneness in object-oriented (OO) components is essential to ensure their practical use in industrial settings. In this paper, we empirically validate three OO metrics suites for their ability to predict software quality in terms of fault-proneness: the Chidamber and Kemerer (CK) metrics, Abreu's Metrics for Object-Oriented Design (MOOD), and Bansiya and Davis' Quality Metrics for Object-Oriented Design (QMOOD). Some CK class metrics have previously been shown to be good predictors of initial OO software quality. However, the other two suites have not been heavily validated except by their original proposers. Here, we explore the ability of these three metrics suites to predict fault-prone classes using defect data for six versions of Rhino, an open-source implementation of JavaScript written in Java. We conclude that the CK and QMOOD suites contain similar components and produce statistical models that are effective in detecting error-prone classes. We also conclude that the class components in the MOOD metrics suite are not good class fault-proneness predictors. Analyzing multivariate binary logistic regression models across six Rhino versions indicates these models may be useful in assessing quality in OO classes produced using modern highly iterative or agile software development processes.
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ISSN:0098-5589
1939-3520
DOI:10.1109/TSE.2007.1015