Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Software Metrics Model For Quality Control
METRICS '97 Proceedings of the 4th International Symposium on Software Metrics
An Adaptive Failure Detection Protocol
PRDC '01 Proceedings of the 2001 Pacific Rim International Symposium on Dependable Computing
Active learning for statistical natural language parsing
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Comparing design and code metrics for software quality prediction
Proceedings of the 4th international workshop on Predictor models in software engineering
A Novel Adaptive Failure Detector for Distributed Systems
NAS '08 Proceedings of the 2008 International Conference on Networking, Architecture, and Storage
IEEE Transactions on Software Engineering
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
An analysis of active learning strategies for sequence labeling tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Background: Software quality prediction plays an important role in improving the quality of software systems. By mining software metrics, predictive models can be induced that provide software managers with insights into quality problems they need to tackle as effectively as possible. Objective: Traditional, supervised learning approaches dominate software quality prediction. Resulting models tend to be project specific. On the other hand, in situations where there are no previous releases, supervised learning approaches are not very useful because large training data sets are needed to develop accurate predictive models. Method: This paper eases the limitations of supervised learning approaches and offers good prediction performance. We propose an adaptive approach in which supervised learning and active learning are coupled together. NaiveBayes classifier is used as the base learner. Results: We track the performance at each iteration of the adaptive learning algorithm and compare it with the performance of supervised learning. Our results show that proposed scheme provides good fault prediction performance over time, i.e., it eventually outperforms the corresponding supervised learning approach. On the other hand, adaptive learning classification approach reduces the variance in prediction performance in comparison with the corresponding supervised learning algorithm. Conclusion: The adaptive approach outperforms the corresponding supervised learning approach when both use Naive-Bayes as base learner. Additional research is needed to investigate whether this observation remains valid with other base classifiers.