Transfer learning for cross-company software defect prediction

Information and Software Technology

Volumn 54, Issue 3, 2012, Pages 248-256

  Ma, Ying ^a   Luo, Guangchun ^a   Zeng, Xue ^a,b   Chen, Aiguo ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b University of California   (United States)

Author keywords

Different distribution; Machine learning; Naive Bayes; Software defect prediction; Transfer learning

Indexed keywords

DIFFERENT DISTRIBUTIONS; MACHINE-LEARNING; NAIVE BAYES; SOFTWARE DEFECT PREDICTION; TRANSFER LEARNING;

CLASSIFIERS; DEFECTS; FORECASTING; INDUSTRY; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS;

SOFTWARE TESTING;

EID: 84855652194     PISSN: 09505849     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.infsof.2011.09.007     Document Type: Article

References (38)

1. L. Briand, V. Basili, and C. Hetmanski Developing interpretable models with optimized set reduction for identifying high risk software components IEEE Transactions on Software Engineering 19 11 1993 1028 1044
2. T.M. Khoshgoftaar, E.B. Allen, J.P. Hudepohl, and S.J. Aud Application of neural networks to software quality modeling of a very large telecommunications system IEEE Transactions on Neural Networks 8 4 1997 902 909 (Pubitemid 127765148)
3. N. Fenton, M. Neil, P. Hearty, W. Marsh, D. Marquez, P. Krause, and R. Mishra Predicting software defects in varying development lifecycles using Bayesian nets Information and Software Technology 49 1 2007 32 43 (Pubitemid 46053772)
4. S. Kanmani, V. Rhymend Uthariaraj, V. Sankaranarayanan, and P. Thambidurai Object-oriented software fault prediction using neural networks Information and Software Technology 49 5 2007 483 492 (Pubitemid 46367627)
5. T. Menzies, J. Greenwald, and A. Frank Data mining static code attributes to learn defect predictors IEEE Transactions on Software Engineering 33 11 2007 2 13 (Pubitemid 46002165)
6. 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.
7. C. Catal, and B. Diri Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem Information Sciences 179 8 2009 1040 1058
8. B. Turhan, T. Menzies, A.B. Bener, and J.D. Stefano On the relative value of cross-company and within-company data for defect prediction Empirical Software Engineering 14 5 2009 540 578
9. 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.
10. 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.
11. G. Boetticher, T. Menzies, T. Ostrand, The PROMISE Repository of Empirical Software Engineering Data, 2007 < http://promisedata.org/repository >.
12. 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.
13. 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.
14. 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.
15. 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.
16. 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.
17. 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. (Pubitemid 350149626)
18. 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.
19. 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. (Pubitemid 40290892)
20. 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.
21. 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.
22. 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.
23. 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.
24. A.R. Gray, and S.G. MacDonnell A Comparison of techniques for developing predictive models of software metrics Information and Software Technology 39 6 1997 425 437 (Pubitemid 127438066)
25. V.K. Vaishnavi, S. Purao, and J. Liegle Object-oriented product metrics: a generic-framework Information Sciences 177 2 2007 587 606
26. J. Al Dallal, L. Briand, An Object-Oriented High-Level Design-Based Class Cohesion Metric, TR, Simula Research Laboratory, 2009.
27. Y. Liu, T.M. Khoshgoftaar, and N. Seliya Evolutionary optimization of software quality modeling with multiple repositories IEEE Transactions on Software Engineering 36 6 2010 852 864
28. R. Duda, P. Hart, N. Nilsson, Subjective bayesian methods for rule-based inference systems, Technical Report 124, Artificial Intelligence Center, SRI International, 1976.
29. L. Peng, B. Yang, Y. Chen, and A. Abraham Data gravitation based classification Information Sciences 179 6 2009 809 819
30. C. Wang, and Y.Q. Chen Improving nearest neighbor classification with simulated gravitational collapse Advances in Natural Computation, Lecture Motes in Computer Science 3612 2005 845 854 (Pubitemid 41449655)
31. 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.
32. I. Newton, Philosophiae Naturalis Principia Mathematica, first ed., Royal Society, London, 1687.
33. E. Frank, M. Hall, B. Pfahringer, Locally Weighted Naive Bayes, in: Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2003, pp. 249-256.
34. 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.
35. I.H. Witten, and E. Frank Data Mining: Practical Machine Learning Tools and Techniques second ed. 2005 Morgan Kaufman San Francisco
36. T. Fawcett An introduction to ROC analysis Pattern Recognition Letters 27 8 2006 861 874
37. C.J. van Rijsbergen Information Retireval second ed. 1979 Butterworth London
38. F. Wilcoxon Individual comparisons by ranking methods Biometrics Bulletin 1 6 1945 80 83