A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Predicting Fault-Prone Software Modules in Telephone Switches
IEEE Transactions on Software Engineering
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Normalization in Support Vector Machines
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Feature selection for text categorization on imbalanced data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
An Improved Categorization of Classifier's Sensitivity on Sample Selection Bias
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Proceedings of the 28th international conference on Software engineering
Mining metrics to predict component failures
Proceedings of the 28th international conference on Software engineering
Constructing informative priors using transfer learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Estimating Location Using Wi-Fi
IEEE Intelligent Systems
Proceedings of the 30th international conference on Software engineering
Predicting defects using network analysis on dependency graphs
Proceedings of the 30th international conference on Software engineering
Topic-bridged PLSA for cross-domain text classification
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Classifying Software Changes: Clean or Buggy?
IEEE Transactions on Software Engineering
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Can developer-module networks predict failures?
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Predicting failures with developer networks and social network analysis
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Predicting faults using the complexity of code changes
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Extracting discriminative concepts for domain adaptation in text mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Improving bug triage with bug tossing graphs
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Fair and balanced?: bias in bug-fix datasets
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
Transferring naive bayes classifiers for text classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Introduction to Machine Learning
Introduction to Machine Learning
Reducing Features to Improve Bug Prediction
ASE '09 Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering
Cross-domain sentiment classification via spectral feature alignment
Proceedings of the 19th international conference on World wide web
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2
IEEE Transactions on Knowledge and Data Engineering
Revisiting common bug prediction findings using effort-aware models
ICSM '10 Proceedings of the 2010 IEEE International Conference on Software Maintenance
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Dealing with noise in defect prediction
Proceedings of the 33rd International Conference on Software Engineering
ReLink: recovering links between bugs and changes
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
High-impact defects: a study of breakage and surprise defects
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Micro interaction metrics for defect prediction
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Network Versus Code Metrics to Predict Defects: A Replication Study
ESEM '11 Proceedings of the 2011 International Symposium on Empirical Software Engineering and Measurement
IEEE Transactions on Software Engineering
Transfer learning for cross-company software defect prediction
Information and Software Technology
Domain Adaptation via Transfer Component Analysis
IEEE Transactions on Neural Networks
Recalling the "imprecision" of cross-project defect prediction
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
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Many software defect prediction approaches have been proposed and most are effective in within-project prediction settings. However, for new projects or projects with limited training data, it is desirable to learn a prediction model by using sufficient training data from existing source projects and then apply the model to some target projects (cross-project defect prediction). Unfortunately, the performance of cross-project defect prediction is generally poor, largely because of feature distribution differences between the source and target projects. In this paper, we apply a state-of-the-art transfer learning approach, TCA, to make feature distributions in source and target projects similar. In addition, we propose a novel transfer defect learning approach, TCA+, by extending TCA. Our experimental results for eight open-source projects show that TCA+ significantly improves cross-project prediction performance.