Robust regression and outlier detection
Robust regression and outlier detection
Instance-Based Learning Algorithms
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
Wrappers for performance enhancement and oblivious decision graphs
Wrappers for performance enhancement and oblivious decision graphs
Evaluating predictive quality models derived from software measures: lessons learned
Journal of Systems and Software
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
The prediction of faulty classes using object-oriented design metrics
Journal of Systems and Software
Bayesian Graphical Models for Software Testing
IEEE Transactions on Software Engineering
Assessing the applicability of fault-proneness models across object-oriented software projects
IEEE Transactions on Software Engineering
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Tree-Based Software Quality Estimation Models For Fault Prediction
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
Prediction of Fault-proneness at Early Phase in Object-Oriented Development
ISORC '99 Proceedings of the 2nd IEEE International Symposium on Object-Oriented Real-Time Distributed Computing
Analyzing Software Measurement Data with Clustering Techniques
IEEE Intelligent Systems
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
Comparing Fault-Proneness Estimation Models
ICECCS '05 Proceedings of the 10th IEEE International Conference on Engineering of Complex Computer Systems
The Accuracy of Fault Prediction in Modified Code - Statistical Model vs. Expert Estimation
ECBS '06 Proceedings of the 13th Annual IEEE International Symposium and Workshop on Engineering of Computer Based Systems
IEEE Transactions on Software Engineering
Number of Faults per Line of Code
IEEE Transactions on Software Engineering
Software Defect Prediction Using Regression via Classification
AICCSA '06 Proceedings of the IEEE International Conference on Computer Systems and Applications
Rule-based machine learning methods for functional prediction
Journal of Artificial Intelligence Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Unsupervised learning for expert-based software quality estimation
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
Estimating the Number of Faults in Code
IEEE Transactions on Software Engineering
Integrating in-process software defect prediction with association mining to discover defect pattern
Information and Software Technology
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Combining techniques for software quality classification: An integrated decision network approach
Expert Systems with Applications: An International Journal
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
Software defect prediction using fuzzy support vector regression
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
The design of polynomial function-based neural network predictors for detection of software defects
Information Sciences: an International Journal
| Hi-index | 12.05 |
In this paper we apply Regression via Classification (RvC) to the problem of estimating the number of software defects. This approach apart from a certain number of faults, it also outputs an associated interval of values, within which this estimate lies with a certain confidence. RvC also allows the production of comprehensible models of software defects exploiting symbolic learning algorithms. To evaluate this approach we perform an extensive comparative experimental study of the effectiveness of several machine learning algorithms in two software data sets. RvC manages to get better regression error than the standard regression approaches on both datasets.