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
Coupling measures and change ripples in C++ application software
Journal of Systems and Software - Special issue on Evaluation and assessment in software engineering
The prediction of faulty classes using object-oriented design metrics
Journal of Systems and Software
The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics
IEEE Transactions on Software Engineering
A Hierarchical Model for Object-Oriented Design Quality Assessment
IEEE Transactions on Software Engineering
A Unified Framework for Cohesion Measurement in Object-OrientedSystems
Empirical Software Engineering
Empirical Software Engineering
Leveraging Legacy System Dollars for E-Business
IT Professional
Does OO Sync with How We Think?
IEEE Software
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 Validation of Object-Oriented Metrics in Two Different Iterative Software Processes
IEEE Transactions on Software Engineering
The Art of Software Testing
Application of neural networks for software quality prediction using object-oriented metrics
Journal of Systems and Software
ACM SIGSOFT Software Engineering Notes
On-line prediction of software reliability using an evolutionary connectionist model
Journal of Systems and Software
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults
IEEE Transactions on Software Engineering
Journal of Systems and Software
ACM SIGSOFT Software Engineering Notes
ACM SIGSOFT Software Engineering Notes
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There have been numerous studies to predict the error proneness of class. If software testers have only a very limited amount of time left to conduct testing, knowing where the most severe errors are likely to occur in a system is more helpful than just knowing where errors are likely to occur. This paper describes how we calculated various object oriented metrics of three versions of Mozilla Firefox. And after that how we collected all the bugs along with their severity levels in these versions of Firefox using Bugzilla database and associated bugs with class. Logistic regression and neural network techniques are followed to predict the error proneness of class under error category. The findings suggest that various metrics can be used to predict error proneness of class under error category. Neural network approach can predict high and medium severity errors more accurately than the low severity errors.