Understanding and Controlling Software Costs
IEEE Transactions on Software Engineering
Boosting a weak learning algorithm by majority
Information and Computation
A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
An Enhanced Neural Network Technique for Software Risk Analysis
IEEE Transactions on Software Engineering
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A Comparative Study of Cost-Sensitive Boosting Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Cost-Sensitive Boosting In Software Quality Modeling
HASE '02 Proceedings of the 7th IEEE International Symposium on High Assurance Systems Engineering
Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study
Empirical Software Engineering
Building Defect Prediction Models in Practice
IEEE Software
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
A Novel Method for Early Software Quality Prediction Based on Support Vector Machine
ISSRE '05 Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Neural Computation
Object-oriented software fault prediction using neural networks
Information and Software Technology
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults
IEEE Transactions on Software Engineering
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
IEEE Transactions on Software Engineering
Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods
IEEE Transactions on Software Engineering
Applying machine learning to software fault-proneness prediction
Journal of Systems and Software
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Neural Networks
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
Hi-index | 12.05 |
Software defect predictors which classify the software modules into defect-prone and not-defect-prone classes are effective tools to maintain the high quality of software products. The early prediction of defect-proneness of the modules can allow software developers to allocate the limited resources on those defect-prone modules such that high quality software can be produced on time and within budget. In the process of software defect prediction, the misclassification of defect-prone modules generally incurs much higher cost than the misclassification of not-defect-prone ones. Most of the previously developed predication models do not consider this cost issue. In this paper, three cost-sensitive boosting algorithms are studied to boost neural networks for software defect prediction. The first algorithm based on threshold-moving tries to move the classification threshold towards the not-fault-prone modules such that more fault-prone modules can be classified correctly. The other two weight-updating based algorithms incorporate the misclassification costs into the weight-update rule of boosting procedure such that the algorithms boost more weights on the samples associated with misclassified defect-prone modules. The performances of the three algorithms are evaluated by using four datasets from NASA projects in terms of a singular measure, the Normalized Expected Cost of Misclassification (NECM). The experimental results suggest that threshold-moving is the best choice to build cost-sensitive software defect prediction models with boosted neural networks among the three algorithms studied, especially for the datasets from projects developed by object-oriented language.