A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Predicting Fault-Prone Software Modules in Telephone Switches
IEEE Transactions on Software Engineering
Managerial Use of Metrics for Object-Oriented Software: An Exploratory Analysis
IEEE Transactions on Software Engineering
Object-oriented design patterns recovery
Journal of Systems and Software
Machine Learning
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
Recovering Traceability Links between Code and Documentation
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
Robust Prediction of Fault-Proneness by Random Forests
ISSRE '04 Proceedings of the 15th International Symposium on Software Reliability Engineering
Reverse Engineering of Object Oriented Code (Monographs in Computer Science)
Reverse Engineering of Object Oriented Code (Monographs in Computer Science)
Mining metrics to predict component failures
Proceedings of the 28th international conference on Software engineering
Predicting component failures at design time
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Toward the Reverse Engineering of UML Sequence Diagrams for Distributed Java Software
IEEE Transactions on Software Engineering
New Frontiers of Reverse Engineering
FOSE '07 2007 Future of Software Engineering
Predicting Defects for Eclipse
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Predicting vulnerable software components
Proceedings of the 14th ACM conference on Computer and communications security
Fault Prediction using Early Lifecycle Data
ISSRE '07 Proceedings of the The 18th IEEE International Symposium on Software Reliability
Implications of ceiling effects in defect predictors
Proceedings of the 4th international workshop on Predictor models in software engineering
Can we build software faster and better and cheaper?
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Revisiting the evaluation of defect prediction models
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Fault detection and prediction in an open-source software project
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Misclassification cost-sensitive fault prediction models
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Design-level metrics estimation based on code metrics
Proceedings of the 2010 ACM Symposium on Applied Computing
An FIS for early detection of defect prone modules
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Variance analysis in software fault prediction models
ISSRE'09 Proceedings of the 20th IEEE international conference on software reliability engineering
Predicting vulnerable software components with dependency graphs
Proceedings of the 6th International Workshop on Security Measurements and Metrics
Which is the right source for vulnerability studies?: an empirical analysis on Mozilla Firefox
Proceedings of the 6th International Workshop on Security Measurements and Metrics
Using traits of web macro scripts to predict reuse
Journal of Visual Languages and Computing
Better, faster, and cheaper: what is better software?
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Programmer-based fault prediction
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
After-life vulnerabilities: a study on firefox evolution, its vulnerabilities, and fixes
ESSoS'11 Proceedings of the Third international conference on Engineering secure software and systems
Impact of test-driven development on productivity, code and tests: A controlled experiment
Information and Software Technology
An iterative semi-supervised approach to software fault prediction
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
An adaptive approach with active learning in software fault prediction
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Influence of confirmation biases of developers on software quality: an empirical study
Software Quality Control
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The prediction of fault-prone modules continues to attract interest due to the significant impact it has on software quality assurance. One of the most important goals of such techniques is to accurately predict the modules where faults are likely to hide as early as possible in the development lifecycle. Design, code, and most recently, requirements metrics have been successfully used for predicting fault-prone modules. The goal of this paper is to compare the performance of predictive models which use design-level metrics with those that use code-level metrics and those that use both. We analyze thirteen datasets from NASA Metrics Data Program which offer design as well as code metrics. Using a range of modeling techniques and statistical significance tests, we confirmed that models built from code metrics typically outperform design metrics based models. However, both types of models prove to be useful as they can be constructed in different project phases. Code-based models can be used to increase the performance of design-level models and, thus, increase the efficiency of assigning verification and validation activities late in the development lifecycle. We also conclude that models that utilize a combination of design and code level metrics outperform models which use either one or the other metric set.