[1] MADHAVJI N H,MIRANSKYY A,KONTOGIANNIS K.Big picture of big data software engineering:with example researchchallenges[C]//Proceedings of the 1stInternational Workshop on BIG Data Software Engineering.Washington D.C.,USA:IEEE Press,2015:11-14. [2] LI Zhiqiang,JING Xiaoyuan,ZHU Xiaoke.Progress on approaches to software defect prediction[J].IET Software,2018,12(3):161-175. [3] CHEN Xiang,GU Qing,LIU Wangshu,et al.Survey of static software defect prediction[J].Journal of Software,2016,27(1):1-25.(in Chinese) 陈翔,顾庆,刘望舒,等.静态软件缺陷预测方法研究[J].软件学报,2016,27(1):1-25. [4] TURHAN B,MENZIES T,BENER A B,et al.On the relative value of cross-company and within-company data for defect prediction[J].Empirical Software Engineering,2009,14(5):540-578. [5] CHEN Xiang,WANG Liping,GU Qing.A survey on cross-project software defect prediction methods[J].Chinese Journal of Computers,2018,41(1):254-274.(in Chinese) 陈翔,王莉萍,顾庆.跨项目软件缺陷预测方法研究综述[J].计算机学报,2018,41(1):254-274. [6] KIM S,ZHANG H,WU R,et al.Dealing with noise in defect prediction[C]//Proceedings of the 33rd International Conference on Software Engineering.Waikiki,USA:[s.n.],2011:481-490. [7] HUANG J,GRETTON A,BORGWARDT K,et al.Correcting sample selection Bias by unlabeled data[M]//SCHÖLKOPF B,PLATT J,HOFMANN T.Advances in Neural Information Processing Systems19:Proceedings of the2006Conference.[S.l.]:MIT Press,2007:601-608. [8] PAN S J,TSANG I W,KWOK J T,et al.Domain adaptation via transfer component analysis[J].IEEE Transactions on Neural Networks,2011,22(2):199-210. [9] HOSSEINI S,TURHAN B,MÄNTYLÄ M J I,et al.A benchmark study on the effectiveness of search-baseddata selection and feature selection for cross project defect prediction[J].Information and Software Technology,2018,95:296-312. [10] MA Ying,LUO Guangchun,ZENG Xue,et al.Transfer learning for cross-company software defect prediction[J].Information and Software Technology,2012,54(3):248-256. [11] WANG Liping,CHEN Xiang,WANG Qiuping,et al.Box-Cox transformation based ensemble learning approach for cross-project software defect prediction[J].Application Research of Computers,2017,34(7):2023-2026.(in Chinese) 王莉萍,陈翔,王秋萍,等.基于Box-Cox转换的集成跨项目软件缺陷预测方法[J].计算机应用研究,2017,34(7):2023-2026. [12] FENG Z,KEIVANLOO I,YING Z.Data transformation in cross-project defect prediction[J].Empirical Software Engineering,2017,22(6):3186-3218. [13] YANG Jie,FAN Guisheng,YU Huiqun.Multi-source heterogeneous software defect prediction method[J].Journal of Chinese Computer Systems,2019,40(4):851-855.(in Chinese) 杨杰,范贵生,虞慧群.一种多源异构软件缺陷预测方法[J].小型微型计算机系统,2019,40(4):851-855. [14] YU Xiao,LIU Jin,FU Mandi,et al.A multi-source tradaboost approach for cross-company defect prediction[C]//Proceedings of the 28th International Conference on Software Engineering and Knowledge Engineering.Redwood City,USA:[s.n.],2016:237-242. [15] XIA X,LO D,PAN S J,et al.Hydra:massively compositional model for cross-project defect prediction[J].IEEE Transactions on Software Engineering,2016,42(10):977-998. [16] QIU Shaojian,LU Lu,JIANG Siyu.Multiple-components weights model for cross-project software defect prediction[J].IET Software,2018,12(4):345-355. [17] ZADROZNY B.Learning and evaluating classifiers under sample selection Bias[C]//Proceedings of International Conference on Machine Learning.New York,USA:ACM Press,2004:1-5. [18] SHIMODAIRA H.Improving predictive inference under covariate shift by weighting the log-likelihood function[J].Journal of Statistical Planning and Inference,2000,90(2):227-244. [19] DAI Wenyuan,YANG Qiang,XUE Gonggui,et al.Boosting for transfer learning[C]//Proceedings of International Conference on Machine Learning.New York,USA:ACM Press,2007:1-5. [20] BORGWARDT K M,GRETTON A,RASCH M J,et al.Integrating structured biological data by kernel maximum mean discrepancy[J].Bioinformatics,2006,22(14):49-57. [21] JURECZKO M,MADEYSKI L.Towards identifying software project clusters with regard to defect prediction[C]//Proceedings of the 6th International Conference on Predictive Models in Software Engineering.Turin,Italy:[s.n.],2010:1-10. [22] CHAWLA N V,BOWYER K W,HALL L O,et al.SMOTE:synthetic minority over-sampling technique[J].Journal of Artificial Intelligence Research,2002,16:321-357. [23] FRIEDMAN M.A comparison of alternative tests of significance for the problem of m rankings[J].Journal of Applied Statistics,1940,11(1):86-92. [24] JANEZ D,DALE S.Statistical comparisons of classifiers over multiple data sets[J].Journal of Machine Learning Research,2006,7:1-30. [25] REYES O,ALTALHI A H,VENTURA S.Statistical comparisons of active learning strategies over multiple datasets[J].Knowledge-Based Systems,2018,145:274-288. [26] ELLIOTT A C,HYNAN L S J C M,BIOMEDICINE P I.A SAS® macro implementation of a multiple comparison post hoc test for a Kruskal-Wallis analysis[J].Computer Methods and Programs in Biomedicine,2011,102(1):75-80. |