A transfer cost-sensitive boosting approach for cross-project defect prediction
Tóm tắt
Từ khóa
Tài liệu tham khảo
Arcuri, A., & Briand, L. (2011). A practical guide for using statistical tests to assess randomized algorithms in software engineering. In 33rd International Conference on Software Engineering (ICSE) (pp. 1–10). doi: 10.1145/1985793.1985795 .
Arisholm, E., Briand, L. C., & Johannessen, E. B. (2010). A systematic and comprehensive investigation of methods to build and evaluate fault prediction models. Journal of Systems and Software, 83(1), 2–17. doi: 10.1016/j.jss.2009.06.055 .
Bansiya, J., & Davis, C. G. (2002). A hierarchical model for object-oriented design quality assessment. IEEE Transactions on Software Engineering, 28(1), 4–17. doi: 10.1109/32.979986 .
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE : Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.
Chen, L., Fang, B., Shang, Z., & Tang, Y. (2015). Negative samples reduction in cross-company software defects prediction. Information and Software Technology, 62, 67–77. doi: 10.1016/j.infsof.2015.01.014 .
Chidamber, S. R., & Kemerer, C. F. (1994). A metrics suite for object oriented design. IEEE Transactions on Software Engineering, 20(6), 476–493. doi: 10.1109/32.295895 .
D’Ambros, M., Lanza, M., & Robbes, R. (2011). Evaluating defect prediction approaches: A benchmark and an extensive comparison. Empirical Software Engineering,. doi: 10.1007/s10664-011-9173-9 .
Dai, W., Yang, Q., Xue, G., & Yu, Y. (2007). Boosting for transfer learning. In Proceedings of the 24th international conference on Machine learning (pp. 193–200). http://dl.acm.org/citation.cfm?id=1273521 . Accessed February 25, 2014.
Dejaeger, K. (2013). Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers. IEEE Transactions on Software Engineering, 39(2), 237–257. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6175912 . Accessed February 25, 2014.
Eaton, E., & DesJardins, M. (2011). Selective transfer between learning tasks using task-based boosting. AAAI, 337–342. http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/viewFile/3752@misc/3915 . Accessed June 11, 2014.
Elish, K. O., & Elish, M. O. (2008). Predicting defect-prone software modules using support vector machines. Journal of Systems and Software, 81(5), 649–660. doi: 10.1016/j.jss.2007.07.040 .
Fan, W., Stolfo, S., Zhang, J., & Chan, P. (1999). AdaCost: misclassification cost-sensitive boosting. ICML. http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:AdaCost+:+Misclassification+Cost-sensitive+Boosting#0 . Accessed November 25, 2014.
Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. doi: 10.1006/jcss.1997.1504 .
Grbac, T., Mausa, G., & Basic, B. (2013). Stability of Software defect prediction in relation to levels of data imbalance. SQAMIA. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.402.8978&rep=rep1&type=pdf . Accessed November 13, 2014.
Hall, T., Beecham, S., Bowes, D., Gray, D., & Counsell, S. (2012). A systematic literature review on fault prediction performance in software engineering. IEEE Transactions on Software Engineering, 38(6), 1276–1304. doi: 10.1109/TSE.2011.103 .
Hall, M., Frank, E., & Holmes, G. (2009). The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18. http://dl.acm.org/citation.cfm?id=1656278 . Accessed November 13, 2014.
He, Z., Shu, F., Yang, Y., Li, M., & Wang, Q. (2011). An investigation on the feasibility of cross-project defect prediction. Automated Software Engineering,. doi: 10.1007/s10515-011-0090-3 .
Henderson-Sellers, B. (1995). Object-oriented metrics: measures of complexity, Prentice-Hall, Inc.
Jureczko, M., & Madeyski, L. (2010). Towards identifying software project clusters with regard to defect prediction. In Proceedings of the 6th international conference on predictive models in software engineering—PROMISE ‘10, 1. doi: 10.1145/1868328.1868342 .
Jureczko, M., & Spinellis, D. (2010). Using object-oriented design metrics to predict software defects. In Models and Methods of System Dependability. Oficyna Wydawnicza Politechniki Wrocławskiej (pp. 69–81).
Ma, Y., Luo, G., Zeng, X., & Chen, A. (2012). Transfer learning for cross-company software defect prediction. Information and Software Technology, 54(3), 248–256. doi: 10.1016/j.infsof.2011.09.007 .
Martin, R. (1994). OO design quality metrics. An analysis of dependencies, 12, 151–170.
McCabe, T. J. (1976). A complexity measure. IEEE Transactions on Software Engineering SE, 2(4), 308–320. doi: 10.1109/TSE.1976.233837 .
Mei-Huei, T., Ming-Hung, K., & Mei-Hwa, C. (1999). An empirical study on object-oriented metrics. In Proceedings sixth international software metrics symposium (Cat. No.PR00403) (pp. 242–249). IEEE Computer Society. doi: 10.1109/METRIC.1999.809745 .
Menzies, T., Caglayan, B., He, Z., Kocaguneli, E., Krall, J., Peters, F., & Turhan, B. (2012). The PROMISE Repository of empirical software engineering data. http://openscience.us/repo/ .
Menzies, T., Dekhtyar, A., Distefano, J., & Greenwald, J. (2007). Problems with precision: A response to “Comments on ‘data mining static code attributes to learn defect predictors’”. IEEE Transactions on Software Engineering,. doi: 10.1109/TSE.2007.70721 .
Menzies, T., Milton, Z., Turhan, B., Cukic, B., Jiang, Y., & Bener, A. (2010). Defect prediction from static code features: Current results, limitations, new approaches. Automated Software Engineering, 17(4), 375–407. doi: 10.1007/s10515-010-0069-5 .
Nam, J., Pan, S. J., & Kim, S. (2013). Transfer defect learning. In 35th International Conference on Software Engineering (ICSE) (pp. 382–391). doi: 10.1109/ICSE.2013.6606584 .
Ryu, D., Choi, O., & Baik, J. (2014). Value-cognitive boosting with a support vector machine for cross-project defect prediction. Empirical Software Engineering. doi: 10.1007/s10664-014-9346-4 .
Shi, X., Fan, W., & Ren, J. (2008). Actively transfer domain knowledge. In Machine Learning and Knowledge Discovery in Databases, (60703110) (pp. 342–357). http://link.springer.com/chapter/10.1007/978-3-540-87481-2_23 . Accessed November 29, 2014.
Singh, Y., Kaur, A., & Malhotra, R. (2009). Empirical validation of object-oriented metrics for predicting fault proneness models. Software Quality Journal, 18(1), 3–35. doi: 10.1007/s11219-009-9079-6 .
Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to data mining. Journal of School Psychology, 19, 51–56. doi: 10.1016/0022-4405(81)90007-8 .
Tomek, I. (1976). Two modifications of CNN. IEEE Transaction Systems, Man and Cybernetics, 769–772. http://ci.nii.ac.jp/naid/80013575533/ . Accessed January 26, 2015.
Turhan, B., Menzies, T., Bener, A. B., & Di Stefano, J. (2009). On the relative value of cross-company and within-company data for defect prediction. Empirical Software Engineering, 14(5), 540–578. doi: 10.1007/s10664-008-9103-7 .
Turhan, B., Tosun Mısırlı, A., & Bener, A. (2013). Empirical evaluation of the effects of mixed project data on learning defect predictors. Information and Software Technology, 55(6), 1101–1118. doi: 10.1016/j.infsof.2012.10.003 .
Vargha, A., & Delaney, H. D. (2000). A critique and improvement of the CL common language effect size statistics of McGraw and Wong. Journal of Educational and Behavioral Statistics,. doi: 10.3102/10769986025002101 .
Wang, S., Chen, H., & Yao, X. (2010). Negative correlation learning for classification ensembles. In The 2010 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). doi: 10.1109/IJCNN.2010.5596702 .
Wang, B. X., & Japkowicz, N. (2009). Boosting support vector machines for imbalanced data sets. Knowledge and Information Systems, 25(1), 1–20. doi: 10.1007/s10115-009-0198-y .
Wang, S., & Yao, X. (2013). Using class imbalance learning for software defect prediction. IEEE Transactions on Reliability, 62(2), 434–443. doi: 10.1109/TR.2013.2259203 .
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80–83. http://www.jstor.org/stable/3001968 . Accessed October 14, 2014.
Yao, Y., & Doretto, G. (2010). Boosting for transfer learning with multiple sources. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, 1855–1862. doi: 10.1109/CVPR.2010.5539857 .
Zimmermann, T., Nagappan, N., Gall, H., Giger, E., & Murphy, B. (2009). Cross-project defect prediction. In Proceedings of the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering (p. 91). doi: 10.1145/1595696.1595713 .