Transfer learning for cross-company software defect prediction

Briand, 1993, Developing interpretable models with optimized set reduction for identifying high risk software components, IEEE Transactions on Software Engineering, 19, 1028, 10.1109/32.256851

Khoshgoftaar, 1997, Application of neural networks to software quality modeling of a very large telecommunications system, IEEE Transactions on Neural Networks, 8, 902, 10.1109/72.595888

Fenton, 2007, Predicting software defects in varying development lifecycles using Bayesian nets, Information and Software Technology, 49, 32, 10.1016/j.infsof.2006.09.001

Kanmani, 2007, Object-oriented software fault prediction using neural networks, Information and Software Technology, 49, 483, 10.1016/j.infsof.2006.07.005

Menzies, 2007, Data mining static code attributes to learn defect predictors, IEEE Transactions on Software Engineering, 33, 2, 10.1109/TSE.2007.256941

T. Menzies, B. Turhan, A. Bener, G. Gay, B. Cukic, Y. Jiang, Implications of ceiling effects in defect predictors, in: Proceedings of the 4th International Workshop on Predictor Models in Software Engineering, 2008, pp. 47–54.

Catal, 2009, Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem, Information Sciences, 179, 1040, 10.1016/j.ins.2008.12.001

Turhan, 2009, On the relative value of cross-company and within-company data for defect prediction, Empirical Software Engineering, 14, 540, 10.1007/s10664-008-9103-7

T. Zimmermann, N. Nagappan, H. Gall, E. Giger, Cross-project defect prediction: a large scale experiment on data vs. domain vs. process, in: Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE), 2009, pp. 91–100.

S.J. Pan, Q. Yang, A survey on transfer learning, Technical Report HKUST-CS 08-08, Department of Computer Science and Engineering, Hong Kong University of Science and Technology, 2008.

G. Boetticher, T. Menzies, T. Ostrand, The PROMISE Repository of Empirical Software Engineering Data, 2007 <http://promisedata.org/repository>.

Y. Shi, Z. Lan, W. Liu, W. Bi, Extending semi-supervised learning methods for inductive transfer learning, In: Ninth IEEE International Conference on Data Mining, 2009, pp. 483–492.

J. Huang, A. Smola, A. Gretton, K.M. Borgwardt, B. Scholkopf, Correcting sample selection bias by unlabeled data, in: Proceedings of the 19th Annual Conference on Neural Information Processing Systems, 2007, pp. 601–608.

S. Bickel, M. Bruckner, T. Scheffer, Discriminative learning for differing training and test distributions, in: Proceedings of the 24th International Conference on Machine Learning, 2007, pp. 81–88.

M. Sugiyama, S. Nakajima, H. Kashima, P.V. Buenau, M. Kawanabe, Direct importance estimation with model selection and its application to covariate shift adaptation, in: Proceedings of the 20th Annual Conference on Neural Information Processing Systems, 2008, pp. 1433–1440.

T. Kamishima, M. Hamasaki, S. Akaho, TrBagg:A Simple Transfer Learning Method and Its Application to Personalization in Collaborative Tagging, in: Proceedings of the IEEE International Conference on Data Mining (ICDM), 2009, pp. 219–228.

W. Dai, G. Xue, Q. Yang, Y. Yu, Transferring naive bayes classifiers for text classification, in: Proceedings of the 22rd AAAI Conference on Artificial Intelligence, 2007, pp. 540–545.

W. Dai, Q. Yang, G. Xue, Y. Yu, Boosting for transfer learning, in: Proceedings of the 24th International Conference on Machine Learning, 2007, pp. 193–200.

P. Wu, T.G. Dietterich, Improving svm accuracy by training on auxiliary data sources, in: Proceedings of the 21st International Conference on Machine Learning, 2004, pp. 871–878.

A. Arnold, R. Nallapati, W.W. Cohen, A comparative study of methods for transductive transfer learning, in: Proceedings of the IEEE International Conference on Data Mining (ICDM), 2007, pp. 77–82.

X. Ling, G.-R. Xue, W. Dai, Y. Jiang, Q. Yang, Y. Yu, Can Chinese web pages be classified with english data source? in: Proceedings of the 17th International Conference on World Wide Web, 2008, pp. 969–978.

J. Blitzer, R. McDonald, F. Pereira, Domain adaptation with structural correspondence learning, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2006, pp. 120–128.

X. Zhang, W. Dai, G. Xue, Y. Yu, Adaptive Email Spam Filtering based on Information Theory, in: Proceedings of the Eighth International Conference on Web Information Systems Engineering (WISE), 2007, pp. 59–170.

Gray, 1997, A Comparison of techniques for developing predictive models of software metrics, Information and Software Technology, 39, 425, 10.1016/S0950-5849(96)00006-7

Vaishnavi, 2007, Object-oriented product metrics: a generic-framework, Information Sciences, 177, 587, 10.1016/j.ins.2006.06.002

J. Al Dallal, L. Briand, An Object-Oriented High-Level Design-Based Class Cohesion Metric, TR, Simula Research Laboratory, 2009.

Liu, 2010, Evolutionary optimization of software quality modeling with multiple repositories, IEEE Transactions on Software Engineering, 36, 852, 10.1109/TSE.2010.51

R. Duda, P. Hart, N. Nilsson, Subjective bayesian methods for rule-based inference systems, Technical Report 124, Artificial Intelligence Center, SRI International, 1976.

Peng, 2009, Data gravitation based classification, Information Sciences, 179, 809, 10.1016/j.ins.2008.11.007

Wang, 2005, Improving nearest neighbor classification with simulated gravitational collapse, Advances in Natural Computation, Lecture Motes in Computer Science, 3612, 845, 10.1007/11539902_104

M. Indulska, M.E. Orlowska, Gravity based spatial clustering, in: Proceedings of the 10th ACM International Symposium on Advances in Geographic Information Systems, 2002, pp. 125–130.

I. Newton, Philosophiae Naturalis Principia Mathematica, first ed., Royal Society, London, 1687.

E. Frank, M. Hall, B. Pfahringer, Locally Weighted Naive Bayes, in: Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2003, pp. 249–256.

U.M. Fayyad, K.B. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, In: Proceedings of the Thirteenth International Joint Conference on Articial Intelligence, 1993, pp. 1022–1027.

Witten, 2005

Fawcett, 2006, An introduction to ROC analysis, Pattern Recognition Letters, 27, 861, 10.1016/j.patrec.2005.10.010

van Rijsbergen, 1979

Wilcoxon, 1945, Individual comparisons by ranking methods, Biometrics Bulletin, 1, 80, 10.2307/3001968