When a software measure is not a measure
Software Engineering Journal
Object-oriented metrics that predict maintainability
Journal of Systems and Software - Special issue on object-oriented software
A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Managerial Use of Metrics for Object-Oriented Software: An Exploratory Analysis
IEEE Transactions on Software Engineering
Another metric suite for object-oriented programming
Journal of Systems and Software
Exploring the relationship between design measures and software quality in object-oriented systems
Journal of Systems and Software
Coupling measures and change ripples in C++ application software
Journal of Systems and Software - Special issue on Evaluation and assessment in software engineering
Art of Software Testing
A Unified Framework for Cohesion Measurement in Object-OrientedSystems
Empirical Software Engineering
Leveraging Legacy System Dollars for E-Business
IT Professional
Does OO Sync with How We Think?
IEEE Software
Software Measurement: A Necessary Scientific Basis
IEEE Transactions on Software Engineering
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
An Empirical Investigation of an Object-Oriented Software System
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
An Empirical Study on Object-Oriented Metrics
METRICS '99 Proceedings of the 6th International Symposium on Software Metrics
An Empirical Validation of Object-Oriented Metrics in Two Different Iterative Software Processes
IEEE Transactions on Software Engineering
Empirical Software Engineering
Journal of Systems and Software
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
The quarks of object-oriented development
Communications of the ACM - Next-generation cyber forensics
Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults
IEEE Transactions on Software Engineering
Improving the usability of the source code quality index with interchangeable metrics sets
Information Processing Letters
On the ability of complexity metrics to predict fault-prone classes in object-oriented systems
Journal of Systems and Software
Finding software metrics threshold values using ROC curves
Journal of Software Maintenance and Evolution: Research and Practice
Information and Software Technology
Software metrics reduction for fault-proneness prediction of software modules
NPC'10 Proceedings of the 2010 IFIP international conference on Network and parallel computing
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
Ownership, experience and defects: a fine-grained study of authorship
Proceedings of the 33rd International Conference on Software Engineering
Software fault prediction for object oriented systems: a literature review
ACM SIGSOFT Software Engineering Notes
ACM SIGSOFT Software Engineering Notes
ACM SIGSOFT Software Engineering Notes
International Journal of Computer Applications in Technology
Critical components testing using hybrid genetic algorithm
ACM SIGSOFT Software Engineering Notes
ACM SIGSOFT Software Engineering Notes
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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Many empirical studies have found that software metrics can predict class error proneness and the prediction can be used to accurately group error-prone classes. Recent empirical studies have used open source systems. These studies, however, focused on the relationship between software metrics and class error proneness during the development phase of software projects. Whether software metrics can still predict class error proneness in a system's post-release evolution is still a question to be answered. This study examined three releases of the Eclipse project and found that although some metrics can still predict class error proneness in three error-severity categories, the accuracy of the prediction decreased from release to release. Furthermore, we found that the prediction cannot be used to build a metrics model to identify error-prone classes with acceptable accuracy. These findings suggest that as a system evolves, the use of some commonly used metrics to identify which classes are more prone to errors becomes increasingly difficult and we should seek alternative methods (to the metric-prediction models) to locate error-prone classes if we want high accuracy.