Towards a metrics suite for object oriented design
OOPSLA '91 Conference proceedings on Object-oriented programming systems, languages, and applications
Object-oriented metrics that predict maintainability
Journal of Systems and Software - Special issue on object-oriented software
Object-oriented software metrics: a practical guide
Object-oriented software metrics: a practical guide
A software complexity model of object-oriented systems
Decision Support Systems - Special issue on information technologies and systems
Multivariate data analysis (4th ed.): with readings
Multivariate data analysis (4th ed.): with readings
Cohesion and reuse in an object-oriented system
SSR '95 Proceedings of the 1995 Symposium on Software reusability
Object-oriented metrics: measures of complexity
Object-oriented metrics: measures of complexity
A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
An Evaluation of the MOOD Set of Object-Oriented Software Metrics
IEEE Transactions on Software Engineering
Managerial Use of Metrics for Object-Oriented Software: An Exploratory Analysis
IEEE Transactions on Software Engineering
A Unified Framework for Coupling Measurement in Object-Oriented Systems
IEEE Transactions on Software Engineering
Proceedings of the 20th international conference on Software engineering
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Exploring the relationship between design measures and software quality in object-oriented systems
Journal of Systems and Software
Data mining: concepts and techniques
Data mining: concepts and techniques
The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics
IEEE Transactions on Software Engineering
A Unified Framework for Cohesion Measurement in Object-OrientedSystems
Empirical Software Engineering
Replicated Case Studies for Investigating Quality Factorsin Object-Oriented Designs
Empirical Software Engineering
Practical Guide to Software Quality Mangement
Practical Guide to Software Quality Mangement
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
Predicting Fault-Proneness using OO Metrics: An Industrial Case Study
CSMR '02 Proceedings of the 6th European Conference on Software Maintenance and Reengineering
Cost-Sensitive Boosting In Software Quality Modeling
HASE '02 Proceedings of the 7th IEEE International Symposium on High Assurance Systems Engineering
An Empirical Study on Object-Oriented Metrics
METRICS '99 Proceedings of the 6th International Symposium on Software Metrics
Logistic regression and artificial neural network classification models: a methodology review
Journal of Biomedical Informatics
Software Quality Engineering: Testing, Quality Assurance, and Quantifiable Improvement
Software Quality Engineering: Testing, Quality Assurance, and Quantifiable Improvement
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
Software Reuse Metrics for Object-Oriented Systems
SERA '05 Proceedings of the Third ACIS Int'l Conference on Software Engineering Research, Management and Applications
Software Measurement and Estimation: A Practical Approach (Quantitative Software Engineering Series)
Software Measurement and Estimation: A Practical Approach (Quantitative Software Engineering Series)
Predicting risky modules in open-source software for high-performance computing
Proceedings of the second international workshop on Software engineering for high performance computing system applications
Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults
IEEE Transactions on Software Engineering
Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods
IEEE Transactions on Software Engineering
Software Process: Improvement and Practice
IEEE Transactions on Neural Networks
Software fault prediction for object oriented systems: a literature review
ACM SIGSOFT Software Engineering Notes
ACM SIGSOFT Software Engineering Notes
Evaluating the effect of control flow on the unit testing effort of classes: an empirical analysis
Advances in Software Engineering
On the relationship between use cases and test suites size: an exploratory study
ACM SIGSOFT Software Engineering Notes
A study of subgroup discovery approaches for defect prediction
Information and Software Technology
An in-depth study of the potentially confounding effect of class size in fault prediction
ACM Transactions on Software Engineering and Methodology (TOSEM)
A survey of computational intelligence approaches for software reliability prediction
ACM SIGSOFT Software Engineering Notes
Applications of fuzzy integrals for predicting software fault-prone
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Empirical validation of software metrics used to predict software quality attributes is important to ensure their practical relevance in software organizations. The aim of this work is to find the relation of object-oriented (OO) metrics with fault proneness at different severity levels of faults. For this purpose, different prediction models have been developed using regression and machine learning methods. We evaluate and compare the performance of these methods to find which method performs better at different severity levels of faults and empirically validate OO metrics given by Chidamber and Kemerer. The results of the empirical study are based on public domain NASA data set. The performance of the predicted models was evaluated using Receiver Operating Characteristic (ROC) analysis. The results show that the area under the curve (measured from the ROC analysis) of models predicted using high severity faults is low as compared with the area under the curve of the model predicted with respect to medium and low severity faults. However, the number of faults in the classes correctly classified by predicted models with respect to high severity faults is not low. This study also shows that the performance of machine learning methods is better than logistic regression method with respect to all the severities of faults. Based on the results, it is reasonable to claim that models targeted at different severity levels of faults could help for planning and executing testing by focusing resources on fault-prone parts of the design and code that are likely to cause serious failures.