COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Machine Learning
Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Bootstrapping statistical parsers from small datasets
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Building Defect Prediction Models in Practice
IEEE Software
Mining metrics to predict component failures
Proceedings of the 28th international conference on Software engineering
Enhancing relevance feedback in image retrieval using unlabeled data
ACM Transactions on Information Systems (TOIS)
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Predicting Faults from Cached History
ICSE '07 Proceedings of the 29th international conference on Software Engineering
Predicting Defects for Eclipse
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Semisupervised Regression with Cotraining-Style Algorithms
IEEE Transactions on Knowledge and Data Engineering
Comments on "Data Mining Static Code Attributes to Learn Defect Predictors"
IEEE Transactions on Software Engineering
Predicting Defective Software Components from Code Complexity Measures
PRDC '07 Proceedings of the 13th Pacific Rim International Symposium on Dependable Computing
Predicting defects using network analysis on dependency graphs
Proceedings of the 30th international conference on Software engineering
On multi-view active learning and the combination with semi-supervised learning
Proceedings of the 25th international conference on Machine learning
IEEE Transactions on Software Engineering
Semi-supervised document retrieval
Information Processing and Management: an International Journal
Predicting faults using the complexity of code changes
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
When Semi-supervised Learning Meets Ensemble Learning
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
On the relative value of cross-company and within-company data for defect prediction
Empirical Software Engineering
COLT'07 Proceedings of the 20th annual conference on Learning theory
Semi-Supervised Learning
Semi-supervised learning by disagreement
Knowledge and Information Systems
On the value of learning from defect dense components for software defect prediction
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
ICSM '10 Proceedings of the 2010 IEEE International Conference on Software Maintenance
Software defect detection with rocus
Journal of Computer Science and Technology
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Software defect prediction using semi-supervised learning with dimension reduction
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
A cost-effectiveness criterion for applying software defect prediction models
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
Software defect prediction using relational association rule mining
Information Sciences: an International Journal
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Software defect prediction can help us better understand and control software quality. Current defect prediction techniques are mainly based on a sufficient amount of historical project data. However, historical data is often not available for new projects and for many organizations. In this case, effective defect prediction is difficult to achieve. To address this problem, we propose sample-based methods for software defect prediction. For a large software system, we can select and test a small percentage of modules, and then build a defect prediction model to predict defect-proneness of the rest of the modules. In this paper, we describe three methods for selecting a sample: random sampling with conventional machine learners, random sampling with a semi-supervised learner and active sampling with active semi-supervised learner. To facilitate the active sampling, we propose a novel active semi-supervised learning method ACoForest which is able to sample the modules that are most helpful for learning a good prediction model. Our experiments on PROMISE datasets show that the proposed methods are effective and have potential to be applied to industrial practice.