Software engineering metrics and models
Software engineering metrics and models
Object-oriented software metrics: a practical guide
Object-oriented software metrics: a practical guide
A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Types of software evolution and software maintenance
Journal of Software Maintenance: Research and Practice
The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics
IEEE Transactions on Software Engineering
Modelling fault-proneness statistically over a sequence of releases: a case study
Journal of Software Maintenance: Research and Practice
An empirical evaluation of fault-proneness models
Proceedings of the 24th International Conference on Software Engineering
Future trends in software evolution metrics
IWPSE '01 Proceedings of the 4th International Workshop on Principles of Software Evolution
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
Assessing the applicability of fault-proneness models across object-oriented software projects
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
Towards Constructing a Class Evolution Model
APSEC '97 Proceedings of the Fourth Asia-Pacific Software Engineering and International Computer Science Conference
A Study on Fault-Proneness Detection of Object-Oriented Systems
CSMR '01 Proceedings of the Fifth European Conference on Software Maintenance and Reengineering
Comments on "The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics"
IEEE Transactions on Software Engineering
An Empirical Validation of Object-Oriented Metrics in Two Different Iterative Software Processes
IEEE Transactions on Software Engineering
Metrics Suite for Class Complexity
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II - Volume 02
Comparing Fault-Proneness Estimation Models
ICECCS '05 Proceedings of the 10th IEEE International Conference on Engineering of Complex Computer Systems
Predicting the Probability of Change in Object-Oriented Systems
IEEE Transactions on Software Engineering
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
On the ability of complexity metrics to predict fault-prone classes in object-oriented systems
Journal of Systems and Software
Software metrics reduction for fault-proneness prediction of software modules
NPC'10 Proceedings of the 2010 IFIP international conference on Network and parallel computing
Reducing test effort: A systematic mapping study on existing approaches
Information and Software Technology
Towards mining informal online data to guide component-reuse decisions
Proceedings of the 16th International ACM Sigsoft symposium on Component-based software engineering
An in-depth study of the potentially confounding effect of class size in fault prediction
ACM Transactions on Software Engineering and Methodology (TOSEM)
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Empirical studies have shown complexity metrics to be good predictors of testing effort and maintainability in traditional, imperative programming languages. Empirical validation studies have also shown that complexity is a good predictor of initial quality and reliability in object-oriented (OO) software. To date, one of the most empirically validated OO complexity metrics is the Chidamber and Kemerer Weighted Methods in a Class (WMC). However, there are many more OO complexity metrics whose predictive power has not been as extensively explored. In this study, we explore the predictive ability of several complexity-related metrics for OO software that have not been heavily validated. We do this by exploring their ability to measure quality in an evolutionary software process, by correlating these metrics to defect data for six versions of Rhino, an open-source implementation of JavaScript written in Java. Using statistical techniques such as Spearman's correlation, principal component analysis, binary logistic regression models and their respective validations, we show that some lesser known complexity metrics including Michura et al.'s standard deviation method complexity and Etzkorn et al.'s average method complexity are more consistent predictors of OO quality than any variant of the Chidamber and Kemerer WMC metric. We also show that these metrics are useful in identifying fault-prone classes in software developed using highly iterative or agile software development processes. Copyright © 2008 John Wiley & Sons, Ltd.