Collaborative filtering based recommendation of sampling methods for software defect prediction
The performance of software defect prediction have been hindered by the imbalanced nature of software defect data. Fortunately, a variety of sampling methods have been employed to improve defect prediction performance. However, researchers and practitioners are usually burdened with selecting the op...
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Published in | Applied soft computing Vol. 90; p. 106163 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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Elsevier B.V
01.05.2020
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Abstract | The performance of software defect prediction have been hindered by the imbalanced nature of software defect data. Fortunately, a variety of sampling methods have been employed to improve defect prediction performance. However, researchers and practitioners are usually burdened with selecting the optimal sampling methods for the defect data at hand. In practice, no sampling method has been found to perform best in theory and practice. Therefore it is necessary and valuable to study how to select applicable sampling methods according to the current data characteristics. This paper presents a collaborative filtering based sampling methods recommendation algorithm (CFSR) for automatically recommending applicable sampling methods for the new defect data. CFSR firstly ranks existing sampling methods with historical defect data, and then mines the data similarity between the new and historical defect data with meta-features. Finally, all the information of ranked sampling methods and data similarity are combined to build a recommendation network, with which the user-based collaborative filtering algorithm is employed to recommend appropriate sampling methods for the new defect data. A thorough experiment with five classification algorithms, two prediction performance, five recommendation performance and 12 popular sampling methods was conducted over 20 imbalanced software defect data. The experimental results firstly demonstrate the importance and necessity of present study, and then show that the proposed CFSR method is feasible and effective.
•We validate the importance and necessity of selecting appropriate sampling methods.•We propose a collaborative filtering based method for selecting sampling methods.•We demonstrate the feasibility and effectiveness of the proposed method. |
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AbstractList | The performance of software defect prediction have been hindered by the imbalanced nature of software defect data. Fortunately, a variety of sampling methods have been employed to improve defect prediction performance. However, researchers and practitioners are usually burdened with selecting the optimal sampling methods for the defect data at hand. In practice, no sampling method has been found to perform best in theory and practice. Therefore it is necessary and valuable to study how to select applicable sampling methods according to the current data characteristics. This paper presents a collaborative filtering based sampling methods recommendation algorithm (CFSR) for automatically recommending applicable sampling methods for the new defect data. CFSR firstly ranks existing sampling methods with historical defect data, and then mines the data similarity between the new and historical defect data with meta-features. Finally, all the information of ranked sampling methods and data similarity are combined to build a recommendation network, with which the user-based collaborative filtering algorithm is employed to recommend appropriate sampling methods for the new defect data. A thorough experiment with five classification algorithms, two prediction performance, five recommendation performance and 12 popular sampling methods was conducted over 20 imbalanced software defect data. The experimental results firstly demonstrate the importance and necessity of present study, and then show that the proposed CFSR method is feasible and effective.
•We validate the importance and necessity of selecting appropriate sampling methods.•We propose a collaborative filtering based method for selecting sampling methods.•We demonstrate the feasibility and effectiveness of the proposed method. |
ArticleNumber | 106163 |
Author | Sun, Zhongbin Sun, Heli Zhu, Xiaoyan Zhang, Jingqi |
Author_xml | – sequence: 1 givenname: Zhongbin surname: Sun fullname: Sun, Zhongbin email: zhongbin725@mail.xjtu.edu.cn – sequence: 2 givenname: Jingqi surname: Zhang fullname: Zhang, Jingqi – sequence: 3 givenname: Heli surname: Sun fullname: Sun, Heli – sequence: 4 givenname: Xiaoyan surname: Zhu fullname: Zhu, Xiaoyan |
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Cites_doi | 10.1145/1370788.1370801 10.1007/s11219-016-9342-6 10.1109/TSMCC.2012.2226152 10.1007/s10664-011-9173-9 10.1109/TR.2018.2804922 10.1162/evco.2009.17.3.275 10.1109/TSMCB.2008.2007853 10.1109/TSMCC.2011.2161285 10.1109/TSE.2017.2720603 10.1109/4235.585893 10.1109/TSE.2016.2543218 10.1016/j.eswa.2011.12.043 10.1016/j.eswa.2008.06.108 10.1016/j.patcog.2014.11.014 10.1007/3-540-48229-6_9 10.1109/TSE.2013.6 10.1109/TSE.2008.35 10.1613/jair.953 10.1016/j.infsof.2017.07.004 10.1109/TKDE.2006.17 10.1109/TSE.2007.70721 10.1016/j.ins.2013.07.007 10.1016/j.ins.2018.06.056 10.1016/j.ins.2017.05.008 10.1109/TSMC.1972.4309137 10.1109/ESEM.2007.28 10.1016/j.neucom.2017.03.011 10.1109/ICSE.2013.6606589 10.1109/TSMCA.2009.2027131 10.1109/TR.2013.2259203 10.1609/aaai.v31i1.10739 10.1109/TSE.2010.90 10.1109/TSE.2017.2724538 10.1109/TSE.2018.2877678 10.1145/3180155.3180197 10.1016/j.eswa.2016.12.035 10.1145/1007730.1007735 10.1145/312624.312682 10.1016/j.asoc.2014.11.023 10.1145/2499393.2499395 10.1109/TIT.1968.1054155 10.1109/TSE.2017.2770124 10.1109/ICSE.2013.6606584 10.1109/TSE.2017.2731766 10.1109/TSE.2016.2597849 10.1016/j.neucom.2015.04.120 10.1145/2939672.2939785 10.1109/TSE.2011.103 10.1109/TKDE.2012.232 10.1016/j.asoc.2016.04.032 10.1145/312129.312220 10.1109/ESEM.2017.50 10.1145/3183339 10.1007/11538059_91 10.1016/j.asoc.2015.04.045 10.1145/371920.372071 10.1016/j.asoc.2017.05.043 10.1109/TSE.2002.1019484 10.1007/s10791-009-9123-y 10.1145/1868328.1868342 |
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References | Wan, Xia, Hassan, Lo, Yin, Yang (b39) 2018 F. Rahman, P. Devanbu, How, and why, process metrics are better, in: IEEE International Conference on Software Engineering, 2013, pp. 432–441. López, Fernández, García, Palade, Herrera (b25) 2013; 250 J. Nam, S.J. Pan, S. Kim, Transfer defect learning, in: Proceedings of the 2013 International Conference on Software Engineering, 2013, pp. 382–391. Batista, Prati, Monard (b16) 2004; 6 Briand, Melo, Wust (b46) 2002; 28 Galar, Fernández, Barrenechea, Bustince, Herrera (b22) 2012; 42 Ofek, Rokach, Stern, Shabtai (b60) 2017; 243 Xia, Lo, Pan, Nagappan, Wang (b3) 2016; 42 Wilson (b56) 1972 Bennin, Keung, Phannachitta, Monden, Mensah (b31) 2018; 44 Song, Jia, Shepperd, Ying, Liu (b8) 2011; 37 Liu, Wu, Zhou (b53) 2009; 39 Bennin, Keung, Monden (b9) 2018 Chen, Fang, Shang, Tang (b50) 2018; 26 Haixiang, Yijing, Shang, Mingyun, Yuanyue, Bing (b13) 2017; 73 Tomek (b57) 1976; 6 D’Ambros, Lanza, Robbes (b40) 2012; 17 Chawla, Bowyer, Hall, Kegelmeyer (b29) 2002; 16 P. Domingos, MetaCost: A general method for making classifiers cost-sensitive, in: 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999, pp. 155–164. Barua, Islam, Yao, Murase (b17) 2014; 26 H. Han, W.-Y. Wang, B.-H. Mao, Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning, in: International Conference on Intelligent Computing, 2005, pp. 878–887. López, Fernández, Moreno-Torres, Herrera (b21) 2012; 39 Jing, Wu, Dong, Xu (b52) 2017; 43 Qin, Liu, Xu, Li (b66) 2010; 13 Malhotra (b38) 2015; 27 Wolpert, Macready (b34) 1997; 1 Hosseini, Turhan, Gunarathna (b2) 2019; 45 Menzies, Greenwald, Frank (b6) 2007; 33 Arar, Ayan (b41) 2017; 59 M. Jureczko, L. Madeyski, Towards identifying software project clusters with regard to defect prediction, in: Proceedings of the 6th International Conference on Predictive Models in Software Engineering, 2010, pp. 9–18. B. Sarwar, G. Karypis, J. Konstan, J. Riedl, Item-based collaborative filtering recommendation algorithms, in: Proceedings of the 10th International Conference on World Wide Web, 2001, pp. 285–295. Yen, Lee (b59) 2009; 36 Douzas, Bacao, Last (b18) 2018; 465 T. Chen, C. Guestrin, XGBoost: A scalable tree boosting system, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785–794. T. Menzies, B. Turhan, A. Bener, G. Gay, B. Cukic, Y. Jiang, Implications of ceiling effects in defect predictors, in: International Workshop on Predictor MODELS in Software Engineering, 2008, pp. 47–54. Zhou, Liu (b20) 2006; 18 García, Herrera (b58) 2009; 17 Arar, Ayan (b62) 2015; 33 Wu, Jing, Sun, Sun, Huang, Cui, Sun (b4) 2018; 67 Zhou, Yang, Lu, Chen, Li, Zhao, Qian, Xu (b5) 2018; 27 Sun, Song, Zhu (b14) 2012; 42 Hall, Beecham, Bowes, Gray, Counsell (b1) 2012; 38 Baeza-Yates, Ribeiro (b65) 2011 Lessmann, Baesens, Mues, Pietsch (b7) 2008; 34 Malhotra (b63) 2016; 49 Lin, Tsai, Hu, Jhang (b28) 2017; 409 Herbold, Trautsch, Grabowski (b43) 2018; 44 Seiffert, Khoshgoftaar, Hulse (b51) 2009; 39 Öztürk (b11) 2017; 92 F. Wu, X.-Y. Jing, S. Shan, W. Zuo, J.-Y. Yang, Multiset feature learning for highly imbalanced data classification, in: The 31st AAAI Conference on Artificial Intelligence, 2017, pp. 1583–1589. A. Agrawal, T. Menzies, Is better data better than better data miners?: on the benefits of tuning SMOTE for defect prediction, in: Proceedings of the 40th International Conference on Software Engineering, 2018, pp. 1050–1061. J.L. Herlocker, J.A. Konstan, A. Borchers, J. Riedl, An algorithmic framework for performing collaborative filtering, in: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1999, pp. 230–237. J. Laurikkala, Improving identification of difficult small classes by balancing class distribution, in: Conference on Artificial Intelligence in Medicine in Europe, 2001, pp. 63–66. T. Zimmermann, N. Nagappan, H. Gall, E. Giger, B. Murphy, 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, 2009, pp. 91–100. He, Garcia (b12) 2008 Nam, Fu, Kim, Menzies, Tan (b42) 2018; 44 Peters, Menzies, Gong, Zhang (b48) 2013; 39 K.E. Bennin, J. Keung, A. Monden, P. Phannachitta, S. Mensah, The significant effects of data sampling approaches on software defect prioritization and classification, in: 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 2017, pp. 364–373. M. Kubat, S. Matwin, et al. Addressing the curse of imbalanced training sets: one-sided selection, in: International Conference on Machine Learning, 1997, pp. 179–186. H. He, Y. Bai, E.A. Garcia, S. Li, ADASYN: Adaptive synthetic sampling approach for imbalanced learning, in: IEEE International Joint Conference on Neural Networks, 2008, pp. 1322–1328. Hart (b55) 1968; 14 S. Herbold, Training data selection for cross-project defect prediction, in: Proceedings of the 9th International Conference on Predictive Models in Software Engineering, 2013, pp. 1–10. Sun, Song, Zhu, Sun, Xu, Zhou (b23) 2015; 48 Loyola-González, Martínez-Trinidad, Carrasco-Ochoa, García-Borroto (b32) 2016; 175 Y. Kamei, A. Monden, S. Matsumoto, T. Kakimoto, K.-i. Matsumoto, The effects of over and under sampling on fault-prone module detection, in: First International Symposium on Empirical Software Engineering and Measurement, 2007, pp. 196–204. Wang, Yao (b15) 2013; 62 Douzas (10.1016/j.asoc.2020.106163_b18) 2018; 465 Baeza-Yates (10.1016/j.asoc.2020.106163_b65) 2011 D’Ambros (10.1016/j.asoc.2020.106163_b40) 2012; 17 Qin (10.1016/j.asoc.2020.106163_b66) 2010; 13 10.1016/j.asoc.2020.106163_b49 Hart (10.1016/j.asoc.2020.106163_b55) 1968; 14 López (10.1016/j.asoc.2020.106163_b21) 2012; 39 10.1016/j.asoc.2020.106163_b44 Nam (10.1016/j.asoc.2020.106163_b42) 2018; 44 10.1016/j.asoc.2020.106163_b45 Briand (10.1016/j.asoc.2020.106163_b46) 2002; 28 10.1016/j.asoc.2020.106163_b47 Yen (10.1016/j.asoc.2020.106163_b59) 2009; 36 Menzies (10.1016/j.asoc.2020.106163_b6) 2007; 33 Bennin (10.1016/j.asoc.2020.106163_b9) 2018 Herbold (10.1016/j.asoc.2020.106163_b43) 2018; 44 Öztürk (10.1016/j.asoc.2020.106163_b11) 2017; 92 Seiffert (10.1016/j.asoc.2020.106163_b51) 2009; 39 Chen (10.1016/j.asoc.2020.106163_b50) 2018; 26 Lin (10.1016/j.asoc.2020.106163_b28) 2017; 409 10.1016/j.asoc.2020.106163_b37 Zhou (10.1016/j.asoc.2020.106163_b20) 2006; 18 10.1016/j.asoc.2020.106163_b33 Wolpert (10.1016/j.asoc.2020.106163_b34) 1997; 1 10.1016/j.asoc.2020.106163_b35 10.1016/j.asoc.2020.106163_b36 Arar (10.1016/j.asoc.2020.106163_b62) 2015; 33 Jing (10.1016/j.asoc.2020.106163_b52) 2017; 43 10.1016/j.asoc.2020.106163_b30 Chawla (10.1016/j.asoc.2020.106163_b29) 2002; 16 Barua (10.1016/j.asoc.2020.106163_b17) 2014; 26 Xia (10.1016/j.asoc.2020.106163_b3) 2016; 42 Arar (10.1016/j.asoc.2020.106163_b41) 2017; 59 Sun (10.1016/j.asoc.2020.106163_b23) 2015; 48 Song (10.1016/j.asoc.2020.106163_b8) 2011; 37 Liu (10.1016/j.asoc.2020.106163_b53) 2009; 39 Malhotra (10.1016/j.asoc.2020.106163_b63) 2016; 49 10.1016/j.asoc.2020.106163_b26 10.1016/j.asoc.2020.106163_b27 Peters (10.1016/j.asoc.2020.106163_b48) 2013; 39 Galar (10.1016/j.asoc.2020.106163_b22) 2012; 42 10.1016/j.asoc.2020.106163_b67 10.1016/j.asoc.2020.106163_b24 López (10.1016/j.asoc.2020.106163_b25) 2013; 250 Lessmann (10.1016/j.asoc.2020.106163_b7) 2008; 34 10.1016/j.asoc.2020.106163_b64 Malhotra (10.1016/j.asoc.2020.106163_b38) 2015; 27 Batista (10.1016/j.asoc.2020.106163_b16) 2004; 6 Tomek (10.1016/j.asoc.2020.106163_b57) 1976; 6 Haixiang (10.1016/j.asoc.2020.106163_b13) 2017; 73 Loyola-González (10.1016/j.asoc.2020.106163_b32) 2016; 175 Sun (10.1016/j.asoc.2020.106163_b14) 2012; 42 Zhou (10.1016/j.asoc.2020.106163_b5) 2018; 27 10.1016/j.asoc.2020.106163_b19 Wu (10.1016/j.asoc.2020.106163_b4) 2018; 67 Wan (10.1016/j.asoc.2020.106163_b39) 2018 García (10.1016/j.asoc.2020.106163_b58) 2009; 17 Hall (10.1016/j.asoc.2020.106163_b1) 2012; 38 Ofek (10.1016/j.asoc.2020.106163_b60) 2017; 243 Wang (10.1016/j.asoc.2020.106163_b15) 2013; 62 10.1016/j.asoc.2020.106163_b10 10.1016/j.asoc.2020.106163_b54 Bennin (10.1016/j.asoc.2020.106163_b31) 2018; 44 10.1016/j.asoc.2020.106163_b61 Wilson (10.1016/j.asoc.2020.106163_b56) 1972 He (10.1016/j.asoc.2020.106163_b12) 2008 Hosseini (10.1016/j.asoc.2020.106163_b2) 2019; 45 |
References_xml | – volume: 6 start-page: 20 year: 2004 end-page: 29 ident: b16 article-title: A study of the behavior of several methods for balancing machine learning training data publication-title: ACM SIGKDD Explor. Newsl. contributor: fullname: Monard – volume: 28 start-page: 706 year: 2002 end-page: 720 ident: b46 article-title: Assessing the applicability of fault-proneness models across object-oriented software projects publication-title: IEEE Trans. Softw. Eng. contributor: fullname: Wust – year: 2011 ident: b65 article-title: Modern Information Retrieval contributor: fullname: Ribeiro – start-page: 1 year: 2018 end-page: 35 ident: b9 article-title: On the relative value of data resampling approaches for software defect prediction publication-title: Empir. Softw. Eng. contributor: fullname: Monden – volume: 18 start-page: 63 year: 2006 end-page: 77 ident: b20 article-title: Training cost-sensitive neural networks with methods addressing the class imbalance problem publication-title: IEEE Trans. Knowl. Data Eng. contributor: fullname: Liu – volume: 44 start-page: 534 year: 2018 end-page: 550 ident: b31 article-title: Mahakil: Diversity based oversampling approach to alleviate the class imbalance issue in software defect prediction publication-title: IEEE Trans. Softw. Eng. contributor: fullname: Mensah – volume: 26 start-page: 405 year: 2014 end-page: 425 ident: b17 article-title: MWMOTE–Majority weighted minority oversampling technique for imbalanced data set learning publication-title: IEEE Trans. Knowl. Data Eng. contributor: fullname: Murase – volume: 73 start-page: 220 year: 2017 end-page: 239 ident: b13 article-title: Learning from class-imbalanced data: Review of methods and applications publication-title: Expert Syst. Appl. contributor: fullname: Bing – volume: 409 start-page: 17 year: 2017 end-page: 26 ident: b28 article-title: Clustering-based undersampling in class-imbalanced data publication-title: Inform. Sci. contributor: fullname: Jhang – volume: 6 start-page: 769 year: 1976 end-page: 772 ident: b57 article-title: Two modifications of CNN publication-title: IEEE Trans. Syst. Man Cybern. contributor: fullname: Tomek – volume: 39 start-page: 1054 year: 2013 end-page: 1068 ident: b48 article-title: Balancing privacy and utility in cross-company defect prediction publication-title: IEEE Trans. Softw. Eng. contributor: fullname: Zhang – volume: 45 start-page: 111 year: 2019 end-page: 147 ident: b2 article-title: A systematic literature review and meta-analysis on cross project defect prediction publication-title: IEEE Trans. Softw. Eng. contributor: fullname: Gunarathna – volume: 175 start-page: 935 year: 2016 end-page: 947 ident: b32 article-title: Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases publication-title: Neurocomputing contributor: fullname: García-Borroto – volume: 26 start-page: 97 year: 2018 end-page: 125 ident: b50 article-title: Tackling class overlap and imbalance problems in software defect prediction publication-title: Softw. Qual. J. contributor: fullname: Tang – volume: 16 start-page: 321 year: 2002 end-page: 357 ident: b29 article-title: SMOTE: synthetic minority over-sampling technique publication-title: J. Artificial Intelligence Res. contributor: fullname: Kegelmeyer – volume: 42 start-page: 977 year: 2016 end-page: 998 ident: b3 article-title: HYDRA: Massively compositional model for cross-project defect prediction publication-title: IEEE Trans. Softw. Eng. contributor: fullname: Wang – volume: 33 start-page: 637 year: 2007 end-page: 640 ident: b6 article-title: Data mining static code attributes to learn defect predictors publication-title: IEEE Trans. Softw. Eng. contributor: fullname: Frank – volume: 39 start-page: 539 year: 2009 end-page: 550 ident: b53 article-title: Exploratory undersampling for class-imbalance learning publication-title: IEEE Trans. Syst. Man Cybern. B contributor: fullname: Zhou – start-page: 1 year: 2018 ident: b39 article-title: Perceptions, expectations, and challenges in defect prediction publication-title: IEEE Trans. Softw. Eng. contributor: fullname: Yang – volume: 44 start-page: 811 year: 2018 end-page: 833 ident: b43 article-title: A comparative study to benchmark cross-project defect prediction approaches publication-title: IEEE Trans. Softw. Eng. contributor: fullname: Grabowski – volume: 33 start-page: 263 year: 2015 end-page: 277 ident: b62 article-title: Software defect prediction using cost-sensitive neural network publication-title: Appl. Soft Comput. contributor: fullname: Ayan – volume: 250 start-page: 113 year: 2013 end-page: 141 ident: b25 article-title: An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics publication-title: Inform. Sci. contributor: fullname: Herrera – volume: 43 start-page: 321 year: 2017 end-page: 339 ident: b52 article-title: An improved SDA based defect prediction framework for both within-project and cross-project class-imbalance problems publication-title: IEEE Trans. Softw. Eng. contributor: fullname: Xu – volume: 48 start-page: 1623 year: 2015 end-page: 1637 ident: b23 article-title: A novel ensemble method for classifying imbalanced data publication-title: Pattern Recognit. contributor: fullname: Zhou – volume: 14 start-page: 515 year: 1968 end-page: 516 ident: b55 article-title: The condensed nearest neighbor rule (Corresp.) publication-title: IEEE Trans. Inform. Theory contributor: fullname: Hart – volume: 92 start-page: 17 year: 2017 end-page: 29 ident: b11 article-title: Which type of metrics are useful to deal with class imbalance in software defect prediction? publication-title: Inf. Softw. Technol. contributor: fullname: Öztürk – volume: 67 start-page: 581 year: 2018 end-page: 597 ident: b4 article-title: Cross-project and within-project semisupervised software defect prediction: A unified approach publication-title: IEEE Trans. Reliab. contributor: fullname: Sun – volume: 62 start-page: 434 year: 2013 end-page: 443 ident: b15 article-title: Using class imbalance learning for software defect prediction publication-title: IEEE Trans. Reliab. contributor: fullname: Yao – start-page: 408 year: 1972 end-page: 421 ident: b56 article-title: Asymptotic properties of nearest neighbor rules using edited data publication-title: IEEE Trans. Syst. Man Cybern. contributor: fullname: Wilson – volume: 34 start-page: 485 year: 2008 end-page: 496 ident: b7 article-title: Benchmarking classification models for software defect prediction: A proposed framework and novel findings publication-title: IEEE Trans. Softw. Eng. contributor: fullname: Pietsch – volume: 17 start-page: 275 year: 2009 end-page: 306 ident: b58 article-title: Evolutionary undersampling for classification with imbalanced datasets: proposals and taxonomy publication-title: Evol. Comput. contributor: fullname: Herrera – volume: 38 start-page: 1276 year: 2012 end-page: 1304 ident: b1 article-title: A systematic literature review on fault prediction performance in software engineering publication-title: IEEE Trans. Softw. Eng. contributor: fullname: Counsell – volume: 42 start-page: 1806 year: 2012 end-page: 1817 ident: b14 article-title: Using coding-based ensemble learning to improve software defect prediction publication-title: IEEE Trans. Syst. Man Cybern. C contributor: fullname: Zhu – volume: 49 start-page: 1034 year: 2016 end-page: 1050 ident: b63 article-title: An empirical framework for defect prediction using machine learning techniques with Android software publication-title: Appl. Soft Comput. contributor: fullname: Malhotra – volume: 39 start-page: 1283 year: 2009 end-page: 1294 ident: b51 article-title: Improving software-quality predictions with data sampling and boosting publication-title: IEEE Trans. Syst. Man Cybern. A contributor: fullname: Hulse – volume: 1 start-page: 67 year: 1997 end-page: 82 ident: b34 article-title: No free lunch theorems for optimization publication-title: IEEE Trans. Evol. Comput. contributor: fullname: Macready – volume: 59 start-page: 197 year: 2017 end-page: 209 ident: b41 article-title: A feature dependent Naive–Bayes approach and its application to the software defect prediction problem publication-title: Appl. Soft Comput. contributor: fullname: Ayan – volume: 27 start-page: 504 year: 2015 end-page: 518 ident: b38 article-title: A systematic review of machine learning techniques for software fault prediction publication-title: Appl. Soft Comput. contributor: fullname: Malhotra – volume: 465 start-page: 1 year: 2018 end-page: 20 ident: b18 article-title: Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE publication-title: Inform. Sci. contributor: fullname: Last – volume: 27 start-page: 1 year: 2018 end-page: 51 ident: b5 article-title: How far we have progressed in the journey? An examination of cross-project defect prediction publication-title: ACM Trans. Softw. Eng. Methodol. contributor: fullname: Xu – volume: 37 start-page: 356 year: 2011 end-page: 370 ident: b8 article-title: A general software defect-proneness prediction framework publication-title: IEEE Trans. Softw. Eng. contributor: fullname: Liu – volume: 13 start-page: 346 year: 2010 end-page: 374 ident: b66 article-title: LETOR: A benchmark collection for research on learning to rank for information retrieval publication-title: Inf. Retr. contributor: fullname: Li – start-page: 1263 year: 2008 end-page: 1284 ident: b12 article-title: Learning from imbalanced data publication-title: IEEE Trans. Knowl. Data Eng. contributor: fullname: Garcia – volume: 44 start-page: 874 year: 2018 end-page: 896 ident: b42 article-title: Heterogeneous defect prediction publication-title: IEEE Trans. Softw. Eng. contributor: fullname: Tan – volume: 17 start-page: 531 year: 2012 end-page: 577 ident: b40 article-title: Evaluating defect prediction approaches: A benchmark and an extensive comparison publication-title: Empir. Softw. Eng. contributor: fullname: Robbes – volume: 243 start-page: 88 year: 2017 end-page: 102 ident: b60 article-title: Fast-CBUS: A fast clustering-based undersampling method for addressing the class imbalance problem publication-title: Neurocomputing contributor: fullname: Shabtai – volume: 39 start-page: 6585 year: 2012 end-page: 6608 ident: b21 article-title: Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. open problems on intrinsic data characteristics publication-title: Expert Syst. Appl. contributor: fullname: Herrera – volume: 36 start-page: 5718 year: 2009 end-page: 5727 ident: b59 article-title: Cluster-based under-sampling approaches for imbalanced data distributions publication-title: Expert Syst. Appl. contributor: fullname: Lee – volume: 42 start-page: 463 year: 2012 end-page: 484 ident: b22 article-title: A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches publication-title: IEEE Trans. Syst. Man Cybern. C contributor: fullname: Herrera – ident: 10.1016/j.asoc.2020.106163_b44 doi: 10.1145/1370788.1370801 – volume: 26 start-page: 97 issue: 1 year: 2018 ident: 10.1016/j.asoc.2020.106163_b50 article-title: Tackling class overlap and imbalance problems in software defect prediction publication-title: Softw. Qual. J. doi: 10.1007/s11219-016-9342-6 contributor: fullname: Chen – volume: 42 start-page: 1806 issue: 6 year: 2012 ident: 10.1016/j.asoc.2020.106163_b14 article-title: Using coding-based ensemble learning to improve software defect prediction publication-title: IEEE Trans. Syst. Man Cybern. C doi: 10.1109/TSMCC.2012.2226152 contributor: fullname: Sun – volume: 17 start-page: 531 issue: 4–5 year: 2012 ident: 10.1016/j.asoc.2020.106163_b40 article-title: Evaluating defect prediction approaches: A benchmark and an extensive comparison publication-title: Empir. Softw. Eng. doi: 10.1007/s10664-011-9173-9 contributor: fullname: D’Ambros – volume: 67 start-page: 581 issue: 2 year: 2018 ident: 10.1016/j.asoc.2020.106163_b4 article-title: Cross-project and within-project semisupervised software defect prediction: A unified approach publication-title: IEEE Trans. Reliab. doi: 10.1109/TR.2018.2804922 contributor: fullname: Wu – volume: 17 start-page: 275 issue: 3 year: 2009 ident: 10.1016/j.asoc.2020.106163_b58 article-title: Evolutionary undersampling for classification with imbalanced datasets: proposals and taxonomy publication-title: Evol. Comput. doi: 10.1162/evco.2009.17.3.275 contributor: fullname: García – volume: 39 start-page: 539 issue: 2 year: 2009 ident: 10.1016/j.asoc.2020.106163_b53 article-title: Exploratory undersampling for class-imbalance learning publication-title: IEEE Trans. Syst. Man Cybern. B doi: 10.1109/TSMCB.2008.2007853 contributor: fullname: Liu – volume: 42 start-page: 463 issue: 4 year: 2012 ident: 10.1016/j.asoc.2020.106163_b22 article-title: A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches publication-title: IEEE Trans. Syst. Man Cybern. C doi: 10.1109/TSMCC.2011.2161285 contributor: fullname: Galar – volume: 44 start-page: 874 issue: 9 year: 2018 ident: 10.1016/j.asoc.2020.106163_b42 article-title: Heterogeneous defect prediction publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.2017.2720603 contributor: fullname: Nam – volume: 1 start-page: 67 issue: 1 year: 1997 ident: 10.1016/j.asoc.2020.106163_b34 article-title: No free lunch theorems for optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.585893 contributor: fullname: Wolpert – volume: 42 start-page: 977 issue: 10 year: 2016 ident: 10.1016/j.asoc.2020.106163_b3 article-title: HYDRA: Massively compositional model for cross-project defect prediction publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.2016.2543218 contributor: fullname: Xia – volume: 39 start-page: 6585 issue: 7 year: 2012 ident: 10.1016/j.asoc.2020.106163_b21 article-title: Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. open problems on intrinsic data characteristics publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.12.043 contributor: fullname: López – volume: 36 start-page: 5718 issue: 3 year: 2009 ident: 10.1016/j.asoc.2020.106163_b59 article-title: Cluster-based under-sampling approaches for imbalanced data distributions publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2008.06.108 contributor: fullname: Yen – volume: 48 start-page: 1623 issue: 5 year: 2015 ident: 10.1016/j.asoc.2020.106163_b23 article-title: A novel ensemble method for classifying imbalanced data publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2014.11.014 contributor: fullname: Sun – ident: 10.1016/j.asoc.2020.106163_b27 doi: 10.1007/3-540-48229-6_9 – volume: 39 start-page: 1054 issue: 8 year: 2013 ident: 10.1016/j.asoc.2020.106163_b48 article-title: Balancing privacy and utility in cross-company defect prediction publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.2013.6 contributor: fullname: Peters – volume: 34 start-page: 485 issue: 4 year: 2008 ident: 10.1016/j.asoc.2020.106163_b7 article-title: Benchmarking classification models for software defect prediction: A proposed framework and novel findings publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.2008.35 contributor: fullname: Lessmann – volume: 16 start-page: 321 year: 2002 ident: 10.1016/j.asoc.2020.106163_b29 article-title: SMOTE: synthetic minority over-sampling technique publication-title: J. Artificial Intelligence Res. doi: 10.1613/jair.953 contributor: fullname: Chawla – volume: 92 start-page: 17 year: 2017 ident: 10.1016/j.asoc.2020.106163_b11 article-title: Which type of metrics are useful to deal with class imbalance in software defect prediction? publication-title: Inf. Softw. Technol. doi: 10.1016/j.infsof.2017.07.004 contributor: fullname: Öztürk – volume: 18 start-page: 63 issue: 1 year: 2006 ident: 10.1016/j.asoc.2020.106163_b20 article-title: Training cost-sensitive neural networks with methods addressing the class imbalance problem publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2006.17 contributor: fullname: Zhou – volume: 33 start-page: 637 issue: 1 year: 2007 ident: 10.1016/j.asoc.2020.106163_b6 article-title: Data mining static code attributes to learn defect predictors publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.2007.70721 contributor: fullname: Menzies – volume: 250 start-page: 113 issue: 11 year: 2013 ident: 10.1016/j.asoc.2020.106163_b25 article-title: An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics publication-title: Inform. Sci. doi: 10.1016/j.ins.2013.07.007 contributor: fullname: López – volume: 465 start-page: 1 year: 2018 ident: 10.1016/j.asoc.2020.106163_b18 article-title: Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE publication-title: Inform. Sci. doi: 10.1016/j.ins.2018.06.056 contributor: fullname: Douzas – volume: 409 start-page: 17 year: 2017 ident: 10.1016/j.asoc.2020.106163_b28 article-title: Clustering-based undersampling in class-imbalanced data publication-title: Inform. Sci. doi: 10.1016/j.ins.2017.05.008 contributor: fullname: Lin – start-page: 408 issue: 3 year: 1972 ident: 10.1016/j.asoc.2020.106163_b56 article-title: Asymptotic properties of nearest neighbor rules using edited data publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/TSMC.1972.4309137 contributor: fullname: Wilson – ident: 10.1016/j.asoc.2020.106163_b33 doi: 10.1109/ESEM.2007.28 – volume: 243 start-page: 88 year: 2017 ident: 10.1016/j.asoc.2020.106163_b60 article-title: Fast-CBUS: A fast clustering-based undersampling method for addressing the class imbalance problem publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.03.011 contributor: fullname: Ofek – ident: 10.1016/j.asoc.2020.106163_b10 doi: 10.1109/ICSE.2013.6606589 – volume: 39 start-page: 1283 issue: 6 year: 2009 ident: 10.1016/j.asoc.2020.106163_b51 article-title: Improving software-quality predictions with data sampling and boosting publication-title: IEEE Trans. Syst. Man Cybern. A doi: 10.1109/TSMCA.2009.2027131 contributor: fullname: Seiffert – ident: 10.1016/j.asoc.2020.106163_b30 – volume: 62 start-page: 434 issue: 2 year: 2013 ident: 10.1016/j.asoc.2020.106163_b15 article-title: Using class imbalance learning for software defect prediction publication-title: IEEE Trans. Reliab. doi: 10.1109/TR.2013.2259203 contributor: fullname: Wang – ident: 10.1016/j.asoc.2020.106163_b24 doi: 10.1609/aaai.v31i1.10739 – volume: 37 start-page: 356 issue: 3 year: 2011 ident: 10.1016/j.asoc.2020.106163_b8 article-title: A general software defect-proneness prediction framework publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.2010.90 contributor: fullname: Song – volume: 44 start-page: 811 issue: 9 year: 2018 ident: 10.1016/j.asoc.2020.106163_b43 article-title: A comparative study to benchmark cross-project defect prediction approaches publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.2017.2724538 contributor: fullname: Herbold – start-page: 1 year: 2018 ident: 10.1016/j.asoc.2020.106163_b39 article-title: Perceptions, expectations, and challenges in defect prediction publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.2018.2877678 contributor: fullname: Wan – ident: 10.1016/j.asoc.2020.106163_b45 doi: 10.1145/3180155.3180197 – volume: 73 start-page: 220 year: 2017 ident: 10.1016/j.asoc.2020.106163_b13 article-title: Learning from class-imbalanced data: Review of methods and applications publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2016.12.035 contributor: fullname: Haixiang – volume: 6 start-page: 769 year: 1976 ident: 10.1016/j.asoc.2020.106163_b57 article-title: Two modifications of CNN publication-title: IEEE Trans. Syst. Man Cybern. contributor: fullname: Tomek – start-page: 1 issue: 1 year: 2018 ident: 10.1016/j.asoc.2020.106163_b9 article-title: On the relative value of data resampling approaches for software defect prediction publication-title: Empir. Softw. Eng. contributor: fullname: Bennin – year: 2011 ident: 10.1016/j.asoc.2020.106163_b65 contributor: fullname: Baeza-Yates – volume: 6 start-page: 20 issue: 1 year: 2004 ident: 10.1016/j.asoc.2020.106163_b16 article-title: A study of the behavior of several methods for balancing machine learning training data publication-title: ACM SIGKDD Explor. Newsl. doi: 10.1145/1007730.1007735 contributor: fullname: Batista – ident: 10.1016/j.asoc.2020.106163_b36 doi: 10.1145/312624.312682 – start-page: 1263 issue: 9 year: 2008 ident: 10.1016/j.asoc.2020.106163_b12 article-title: Learning from imbalanced data publication-title: IEEE Trans. Knowl. Data Eng. contributor: fullname: He – volume: 27 start-page: 504 year: 2015 ident: 10.1016/j.asoc.2020.106163_b38 article-title: A systematic review of machine learning techniques for software fault prediction publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2014.11.023 contributor: fullname: Malhotra – ident: 10.1016/j.asoc.2020.106163_b67 doi: 10.1145/2499393.2499395 – volume: 14 start-page: 515 issue: 3 year: 1968 ident: 10.1016/j.asoc.2020.106163_b55 article-title: The condensed nearest neighbor rule (Corresp.) publication-title: IEEE Trans. Inform. Theory doi: 10.1109/TIT.1968.1054155 contributor: fullname: Hart – volume: 45 start-page: 111 issue: 2 year: 2019 ident: 10.1016/j.asoc.2020.106163_b2 article-title: A systematic literature review and meta-analysis on cross project defect prediction publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.2017.2770124 contributor: fullname: Hosseini – ident: 10.1016/j.asoc.2020.106163_b49 doi: 10.1109/ICSE.2013.6606584 – volume: 44 start-page: 534 issue: 6 year: 2018 ident: 10.1016/j.asoc.2020.106163_b31 article-title: Mahakil: Diversity based oversampling approach to alleviate the class imbalance issue in software defect prediction publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.2017.2731766 contributor: fullname: Bennin – volume: 43 start-page: 321 issue: 4 year: 2017 ident: 10.1016/j.asoc.2020.106163_b52 article-title: An improved SDA based defect prediction framework for both within-project and cross-project class-imbalance problems publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.2016.2597849 contributor: fullname: Jing – volume: 175 start-page: 935 year: 2016 ident: 10.1016/j.asoc.2020.106163_b32 article-title: Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.04.120 contributor: fullname: Loyola-González – ident: 10.1016/j.asoc.2020.106163_b37 doi: 10.1145/2939672.2939785 – volume: 38 start-page: 1276 issue: 6 year: 2012 ident: 10.1016/j.asoc.2020.106163_b1 article-title: A systematic literature review on fault prediction performance in software engineering publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.2011.103 contributor: fullname: Hall – ident: 10.1016/j.asoc.2020.106163_b47 – volume: 26 start-page: 405 issue: 2 year: 2014 ident: 10.1016/j.asoc.2020.106163_b17 article-title: MWMOTE–Majority weighted minority oversampling technique for imbalanced data set learning publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2012.232 contributor: fullname: Barua – volume: 49 start-page: 1034 year: 2016 ident: 10.1016/j.asoc.2020.106163_b63 article-title: An empirical framework for defect prediction using machine learning techniques with Android software publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2016.04.032 contributor: fullname: Malhotra – ident: 10.1016/j.asoc.2020.106163_b19 doi: 10.1145/312129.312220 – ident: 10.1016/j.asoc.2020.106163_b54 doi: 10.1109/ESEM.2017.50 – volume: 27 start-page: 1 issue: 1 year: 2018 ident: 10.1016/j.asoc.2020.106163_b5 article-title: How far we have progressed in the journey? An examination of cross-project defect prediction publication-title: ACM Trans. Softw. Eng. Methodol. doi: 10.1145/3183339 contributor: fullname: Zhou – ident: 10.1016/j.asoc.2020.106163_b61 doi: 10.1007/11538059_91 – volume: 33 start-page: 263 year: 2015 ident: 10.1016/j.asoc.2020.106163_b62 article-title: Software defect prediction using cost-sensitive neural network publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.04.045 contributor: fullname: Arar – ident: 10.1016/j.asoc.2020.106163_b35 doi: 10.1145/371920.372071 – ident: 10.1016/j.asoc.2020.106163_b26 – volume: 59 start-page: 197 year: 2017 ident: 10.1016/j.asoc.2020.106163_b41 article-title: A feature dependent Naive–Bayes approach and its application to the software defect prediction problem publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2017.05.043 contributor: fullname: Arar – volume: 28 start-page: 706 issue: 7 year: 2002 ident: 10.1016/j.asoc.2020.106163_b46 article-title: Assessing the applicability of fault-proneness models across object-oriented software projects publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.2002.1019484 contributor: fullname: Briand – volume: 13 start-page: 346 issue: 4 year: 2010 ident: 10.1016/j.asoc.2020.106163_b66 article-title: LETOR: A benchmark collection for research on learning to rank for information retrieval publication-title: Inf. Retr. doi: 10.1007/s10791-009-9123-y contributor: fullname: Qin – ident: 10.1016/j.asoc.2020.106163_b64 doi: 10.1145/1868328.1868342 |
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