Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (5): 1039-1062.doi: 10.1007/s11390-019-1959-z
Special Issue: Data Management and Data Mining; Software Systems
• Software Systems • Previous Articles Next Articles
Zhou Xu1,2,3, Shuai Pang2, Tao Zhang1,4*, Senior Member, CCF, Xia-Pu Luo3*, Member, ACM, IEEE, Jin Liu2,4,5, Member, CCF, IEEE, Yu-Tian Tang3, Xiao Yu2,6, Lei Xue3, Member, IEEE
|  Mei H. Understanding "software-defined" from an OS perspective:Technical challenges and research issues. Sci. China-Inf. Sci., 2017, 60(12):Article No. 126101.
 Lyu M R. Handbook of Software Reliability Engineering. McGraw-Hill, 1996.
 Xu Z, Xuan J, Liu J, Cui X. MICHAC:Defect prediction via feature selection based on maximal information coefficient with hierarchical agglomerative clustering. In Proc. the 23rd Int. Conf. Software Analysis, Evolution, and Reengineering, March 2016, pp.370-381.
 Ni C, Liu W S, Chen X, Gu Q, Chen D, Huang G D. A cluster based feature selection method for cross-project software defect prediction. J. Comput. Sci. Technol., 2017, 32(6):1090-1107.
 Ma Y, Luo G, Zeng X, Chen A. Transfer learning for crosscompany software defect prediction. Inf. Softw. Technol., 2012, 54(3):248-256.
 Nam J, Pan S J, Kim S. Transfer defect learning. In Proc. the 35th Int. Conf. Software Engineering, May 2013, pp.382-391.
 Wang J, Chen Y, Hao S, Feng W, Shen Z. Balanced distribution adaptation for transfer learning. In Proc. the 17th Int. Conf. Data Mining, November 2017, pp.1129-1134.
 Menzies T, Greenwald J, Frank A. Data mining static code attributes to learn defect predictors. IEEE Trans. Softw. Eng., 2007, 33(1):2-13.
 Fawcett T. An introduction to ROC analysis. Pattern Recognit. Lett., 2006, 27(8):861-874.
 Huang Q, Xia X, Lo D. Supervised vs unsupervised models:A holistic look at effort-aware just-in-time defect prediction. In Proc. the 2017 Int. Conf. Software Maintenance and Evolution, September 2017, pp.159-170.
 Xu Z, Li S, Tang Y et al. Cross version defect prediction with representative data via sparse subset selection. In Proc. the 26th Int. Conf. Program Comprehension, May 2018, pp.132-143.
 Briand L C, Melo W L, Wüst J. Assessing the applicability of fault-proneness models across object-oriented software projects. IEEE Trans. Softw. Eng., 2002, 28(7):706-720.
 Zimmermann T, Nagappan N, Gall H, Giger E, Murphy B. Cross-project defect prediction:A large scale experiment on data vs. domain vs. process. In Proc. the 7th Joint Meeting of the European Software Engineering Conf. and the ACM SIGSOFT Symp. Foundations of Software Engineering, August 2009, pp.91-100.
 Turhan B, Menzies T, Bener A B, di Stefano J. On the relative value of cross-company and within-company data for defect prediction. Empir. Softw. Eng., 2009, 14(5):540-578.
 Peters F, Menzies T, Marcus A. Better cross company defect prediction. In Proc. the 10th Working Conf. Mining Software Repositories, May 2013, pp.409-418.
 Kawata K, Amasaki S, Yokogawa T. Improving relevancy filter methods for cross-project defect prediction. In Proc. the 3rd Int. Conf. Applied Computing and Information Technology, July 2015, pp.1-12.
 Yu X, Zhang J, Zhou P, Liu J. A data filtering method based on agglomerative clustering. In Proc. the 29th Int. Conf. Software Engineering and Knowledge Engineering, July 2017, pp.392-397.
 He P, Li B, Zhang D, Ma Y. Simplification of training data for cross-project defect prediction. arXiv:1405.0773, 2014. https://arxiv.org/abs/1405.0773, June 2019.
 He P, Ma Y, Li B. TDSelector:A training data selection method for cross-project defect prediction. arXiv:1612.09065, 2016. https://arxiv.org/abs/1612.09065, Jun. 2019.
 He P, He Y, Yu L, Li B. An improved method for cross-project defect prediction by simplifying training data. Math. Probl. Eng., 2018, 2018:Article No. 2650415.
 Chen L, Fang B, Shang Z, Tang Y. Negative samples reduction in cross-company software defects prediction. Inf. Softw. Technol., 2015, 62:67-77.
 Ryu D, Jang J I, Baik J. A transfer cost-sensitive boosting approach for cross-project defect prediction. Softw. Qual. J., 2017, 25(1):235-272.
 Liu C, Yang D, Xia X, Yan M, Zhang X. A two-phase transfer learning model for cross-project defect prediction. Inf. Softw. Technol., 2019, 107:125-136.
 Forbes C, Evans M, Hastings N, Peacock B. Statistical Distributions (4th edition). John Wiley and Sons, 2010.
 Long M, Wang J, Ding G, Sun J, Yu P S. Transfer feature learning with joint distribution adaptation. In Proc. the 2013 IEEE Int. Conf. Computer Vision, December 2013, pp.2200-2207.
 Pan S J, Tsang I W, Kwok J T, Yang Q. Domain adaptation via transfer component analysis. IEEE Trans. Neural Networks, 2011, 22(2):199-210.
 D'Ambros M, Lanza M, Robbes R. Evaluating defect prediction approaches:A benchmark and an extensive comparison. Empir. Softw. Eng., 2012, 17(4/5):531-577.
 Shepperd M, Song Q, Sun Z, Mair C. Data quality:Some comments on the NASA software defect datasets. IEEE Trans. Softw. Eng., 2013, 39(9):1208-1215.
 Lessmann S, Baesens B, Mues C, Pietsch S. Benchmarking classification models for software defect prediction:A proposed framework and novel findings. IEEE Trans. Softw. Eng., 2008, 34(4):485-496.
 Ghotra B, McIntosh S, Hassan A E. Revisiting the impact of classification techniques on the performance of defect prediction models. In Proc. the 37th Int. Conf. Software Engineering, May 2015, pp.789-800.
 Xu Z, Liu J, Luo X, Yang Z, Zhang Y, Yuan P, Tang Y, Zhang T. Software defect prediction based on kernel PCA and weighted extreme learning machine. Inf. Softw. Technol., 2019, 106:182-200.
 Xu Z, Liu J, Yang Z, An G, Jia X. The impact of feature selection on defect prediction performance:An empirical comparison. In Proc. the 27th Int. Symp. Software Reliability Engineering, October 2016, pp.309-320.
 Xu Z, Yuan P, Zhang T, Tang Y, Li S, Xia Z. HDA:Crossproject defect prediction via heterogeneous domain adaptation with dictionary learning. IEEE Access, 2018, 6:57597-57613.
 Jing X Y, Wu F, Dong X, Qi F, Xu B. Heterogeneous crosscompany defect prediction by unified metric representation and CCA-based transfer learning. In Proc. the 10th Joint Meeting on Foundations of Software Engineering, August 31-September 4, 2015, pp.496-507.
 Wu R, Zhang H, Kim S, Cheung S C. ReLink:Recovering links between bugs and changes. In Proc. the 19th ACM SIGSOFT Symp. and the 13th European Conf. Foundations of Software Engineering, September 2011, pp.15-25.
 Han J, Pei J, Kamber M. Data mining:Concepts and Techniques (3rd edition). Morgan Kaufmann, 2011.
 Xia X, David L O, Pan S J, Nagappan N, Wang X. HYDRA:Massively compositional model for cross-project defect prediction. IEEE Trans. Softw. Eng., 2016, 42(10):977-998.
 Yang Y, Zhou Y, Lu H, Chen L, Chen Z, Xu B, Zhang Z. Are slice-based cohesion metrics actually useful in effortaware post-release fault-proneness prediction? An empirical study. IEEE Trans. Softw. Eng., 2015, 41(4):331-357.
 Nam J, Kim S. CLAMI:Defect prediction on unlabeled datasets (T). In Proc. the 30th Int. Conf. Automated Software Engineering, November 2015, pp.452-463.
 Yang Y, Harman M, Krinke J et al. An empirical study on dependence clusters for effort-aware fault-proneness prediction. In Proc. the 31st IEEE/ACM Int. Conf. Automated Software Engineering, September 2016, pp.296-307.
 Nam J, Fu W, Kim S et al. Heterogeneous defect prediction. IEEE Trans. Softw. Eng., 2018, 44(9):874-896.
 Li Z, Jing X Y, Zhu X, Zhang H. Heterogeneous defect prediction through multiple kernel learning and ensemble learning. In Proc. the 2017 Int. Conf. Software Maintenance and Evolution, Sept. 2017, pp.91-102.
 Li Z, Jing X Y, Zhu X, Zhang H, Xu B, Ying S. On the multiple sources and privacy preservation issues for heterogeneous defect prediction. IEEE Trans. Softw. Eng., 2019, 45(4):391-411.
 Li Z, Jing X Y, Wu F, Zhu X, Xu B, Ying S. Costsensitive transfer kernel canonical correlation analysis for heterogeneous defect prediction. Autom. Softw. Eng., 2018, 25(2):201-245.
 Fan R E, Chang K W, Hsieh C J, Wang X R, Lin C J. LIBLINEAR:A library for large linear classification. J. Mach. Learn. Res., 2008, 9:1871-1874.
 Sasaki Y. The truth of the F -measure. Teach Tutor Mater, 2007, 1(5):1-5.
 Jiang Y, Cukic B, Ma Y. Techniques for evaluating fault prediction models. Empir. Softw. Eng., 2008, 13(5):561-595.
 Liparas D, Angelis L, Feldt R. Applying the MahalanobisTaguchi strategy for software defect diagnosis. Autom. Softw. Eng., 2012, 19(2):141-165.
 Jing X Y, Wu F, Dong X, Xu B. An improved SDA based defect prediction framework for both within-project and cross-project class-imbalance problems. IEEE Trans. Softw. Eng., 2017, 43(4):321-339.
 Wang S, Yao X. Using class imbalance learning for software defect prediction. IEEE Trans. Reliab., 2013, 62(2):434-443.
 Ryu D, Jang J I, Baik J. A hybrid instance selection using nearest-neighbor for cross-project defect prediction. J. Comput. Sci. Technol., 2015, 30(5):969-980.
 Li M, Zhang H, Wu R et al. Sample-based software defect prediction with active and semi-supervised learning. Autom. Softw. Eng., 2012, 19(2):201-230.
 Ling C X, Huang J, Zhang H. AUC:A statistically consistent and more discriminating measure than accuracy. In Proc. the 18th Int. Joint Conf. Artificial Intelligence, August 2003, pp.519-524.
 Huang Q, Xia X, Lo D. Revisiting supervised and unsupervised models for effort-aware just-in-time defect prediction. Empir. Softw. Eng., doi:10.1007/s10664-018-9661-2.
 Demšar J. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res., 2006, 7:1-30.
 Mende T, Koschke R. Effort-aware defect prediction models. In Proc. the 14th European. Conf. Software Maintenance and Reengineering, March 2010, pp.107-116.
 Herbold S, Trautsch A, Grabowski J. A comparative study to benchmark cross-project defect prediction approaches. IEEE Trans. Softw. Eng., 2018, 44(9):811-833.
 Zhou Y, Yang Y, Lu H et al. How far we have progressed in the journey? An examination of cross-project defect prediction. ACM Trans. Software Eng. Method., 2018, 27(1):Article No. 1.
 Tantithamthavorn C, McIntosh S, Hassan A E et al. The impact of automated parameter optimization on defect prediction models. IEEE Trans. Softw. Eng., 2019, 45(7):683-672.
 Shepperd M, Bowes D, Hall T. Researcher bias:The use of machine learning in software defect prediction. IEEE Trans. Softw. Eng., 2014, 40(6):603-616.
 Tantithamthavorn C, McIntosh S, Hassan A E, Matsumoto K. An empirical comparison of model validation techniques for defect prediction models. IEEE Trans. Softw. Eng., 2017, 43(1):1-18.
 Herbold S. Comments on ScottKnottESD in response to "an empirical comparison of model validation techniques for defect prediction models". IEEE Trans. Softw. Eng., 2017, 43(11):1091-1094.
|||Xin-Feng Wang, Xiang Zhou, Jia-Hua Rao, Zhu-Jin Zhang, and Yue-Dong Yang. Imputing DNA Methylation by Transferred Learning Based Neural Network [J]. Journal of Computer Science and Technology, 2022, 37(2): 320-329.|
|||Songjie Niu, Shimin Chen. TransGPerf: Exploiting Transfer Learning for Modeling Distributed Graph Computation Performance [J]. Journal of Computer Science and Technology, 2021, 36(4): 778-791.|
|||Wei Du, Yu Sun, Hui-Min Bao, Liang Chen, Ying Li, Yan-Chun Liang. DeepHBSP: A Deep Learning Framework for Predicting Human Blood-Secretory Proteins Using Transfer Learning [J]. Journal of Computer Science and Technology, 2021, 36(2): 234-247.|
|||Ying Li, Jia-Jie Xu, Peng-Peng Zhao, Jun-Hua Fang, Wei Chen, Lei Zhao. ATLRec: An Attentional Adversarial Transfer Learning Network for Cross-Domain Recommendation [J]. Journal of Computer Science and Technology, 2020, 35(4): 794-808.|
|||Fu-Zhen Zhuang, Ying-Min Zhou, Hao-Chao Ying, Fu-Zheng Zhang, Xiang Ao, Xing Xie, Qing He, Hui Xiong. Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining [J]. Journal of Computer Science and Technology, 2020, 35(2): 305-319.|
|||De-Fu Lian, Qi Liu. Jointly Recommending Library Books and Predicting Academic Performance: A Mutual Reinforcement Perspective [J]. , 2018, 33(4): 654-667.|
|||Chao Ni, Wang-Shu Liu, Xiang Chen, Qing Gu, Dao-Xu Chen, Qi-Guo Huang. A Cluster Based Feature Selection Method for Cross-Project Software Defect Prediction [J]. , 2017, 32(6): 1090-1107.|
|||Yang-Sheng Ji (吉阳生), Jia-Jun Chen (陈家骏), Member, CCF, Gang Niu (牛罡), Lin Shang (商琳), Member, CCF, and Xin-Yu Dai (戴新宇), Member, CCF. Transfer Learning via Multi-View Principal Component Analysis [J]. , 2011, 26(1): 81-98.|