{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T17:51:59Z","timestamp":1785952319306,"version":"3.56.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T00:00:00Z","timestamp":1628294400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T00:00:00Z","timestamp":1628294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Autom Softw Eng"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s10515-021-00285-y","type":"journal-article","created":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T20:02:45Z","timestamp":1628366565000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Predicting the Defects using Stacked Ensemble Learner with Filtered Dataset"],"prefix":"10.1007","volume":"28","author":[{"given":"Somya","family":"Goyal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,7]]},"reference":[{"key":"285_CR1","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.knosys.2014.10.017","volume":"74","author":"G Abaei","year":"2015","unstructured":"Abaei, G., Selamat, A., Fujita, H.: An empirical study based on semi-supervised hybrid self-organizing map for software fault prediction. Knowl. Based Syst. 74, 28\u201339 (2015)","journal-title":"Knowl. Based Syst."},{"issue":"2","key":"285_CR2","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1142\/S0218194012400037","volume":"22","author":"W Afzal","year":"2012","unstructured":"Afzal, W., Torkar, R., Feldt, R.: Resampling methods in software quality classification, nternational. J. Softw. Eng. Knowl. Eng. 22(2), 203\u2013223 (2012)","journal-title":"J. Softw. Eng. Knowl. Eng."},{"key":"285_CR3","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.infsof.2017.11.005","volume":"96","author":"A Boucher","year":"2018","unstructured":"Boucher, A., Badri, M.: Software metrics thresholds calculation techniques to predict fault-proneness: An empirical comparison. Inf. Softw. Technol. 96, 38\u201367 (2018)","journal-title":"Inf. Softw. Technol."},{"issue":"5","key":"285_CR4","doi-asserted-by":"crossref","first-page":"e5478","DOI":"10.1002\/cpe.5478","volume":"32","author":"X Cai","year":"2019","unstructured":"Cai, X., Niu, Y., Geng, S., Zhang, J., Cui, Z., Li, J., Chen, J.: An under-sampled software defect prediction method based on hybrid multi-objective cuckoo search. Concurr. Comput. Pract. Exp. 32(5), e5478 (2019)","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"285_CR5","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s11219-016-9342-6","volume":"26","author":"L Chen","year":"2018","unstructured":"Chen, L., Fang, B., Shang, Z., et al.: Tackling class overlap and imbalance problems in software defect prediction. Softw. Qual J 26, 97\u2013125 (2018). https:\/\/doi.org\/10.1007\/s11219-016-9342-6","journal-title":"Softw. Qual J"},{"issue":"6","key":"285_CR6","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1109\/TSE.2018.2790925","volume":"45","author":"J Chen","year":"2019","unstructured":"Chen, J., Nair, V., Krishna, R., Menzies, T.: \u201cSampling\u201d as a baseline optimizer for search-based software engineering. IEEE Trans. Softw. Eng. 45(6), 597\u2013614 (2019). https:\/\/doi.org\/10.1109\/TSE.2018.2790925","journal-title":"IEEE Trans. Softw. Eng."},{"key":"285_CR7","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1016\/j.eswa.2014.10.025","volume":"42","author":"E Erturk","year":"2015","unstructured":"Erturk, E., Sezer, E.A.: A comparison of some soft computing methods for software fault prediction. Expert Syst. Appl. 42, 1872\u20131879 (2015)","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"285_CR8","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1049\/iet-sen.2018.5193","volume":"13","author":"EA Felix","year":"2019","unstructured":"Felix, E.A., Lee, S.P.: Systematic literature review of preprocessing techniques for imbalanced data. IET Softw. 13(6), 479\u2013496 (2019)","journal-title":"IET Softw."},{"issue":"4","key":"285_CR9","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","volume":"42","author":"M Galar","year":"2011","unstructured":"Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., Herrera, F.: A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans. Syst. Man Cybern. Part C (appl. Rev.) 42(4), 463\u2013484 (2011)","journal-title":"IEEE Trans. Syst. Man Cybern. Part C (appl. Rev.)"},{"issue":"2","key":"285_CR10","doi-asserted-by":"publisher","first-page":"20","DOI":"10.4018\/IJKSS.2020040102","volume":"11","author":"S Goyal","year":"2020","unstructured":"Goyal, S.: Comparison of machine learning techniques for software quality prediction. Int. J. Know. Syst. Sci. (IJKSS) 11(2), 20\u201340 (2020)","journal-title":"Int. J. Know. Syst. Sci. (IJKSS)"},{"key":"285_CR14","doi-asserted-by":"crossref","unstructured":"Goyal, S., Bhatia, P.K.: A non-linear technique for effective software effort estimation using multi-layer perceptrons. In :2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), pp. 1\u20134. IEEE, (2019)","DOI":"10.1109\/COMITCon.2019.8862256"},{"issue":"8","key":"285_CR11","first-page":"637","volume":"18","author":"S Goyal","year":"2019","unstructured":"Goyal, S., Bhatia, P.K.: GA based dimensionality reduction for effective software effort estimation using ANN. Adv. Appl. Math. Sci. 18(8), 637\u2013649 (2019b)","journal-title":"Adv. Appl. Math. Sci."},{"key":"285_CR12","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/978-3-030-30577-2_15","volume-title":"Proceedings of ICETIT 2019","author":"S Goyal","year":"2020","unstructured":"Goyal, S., Bhatia, P.K.: Feature selection technique for effective software effort estimation using multi-layer perceptrons. In: Proceedings of ICETIT 2019, pp. 183\u2013194. Springer, Cham (2020a)"},{"key":"285_CR13","first-page":"35","volume":"3","author":"S Goyal","year":"2018","unstructured":"Goyal, S., Parashar, A.: Machine learning application to improve COCOMO model using neural networks. Int. J. Inform. Technol. Comput. Sci. (IJITCS) 3, 35\u201351 (2018)","journal-title":"Int. J. Inform. Technol. Comput. Sci. (IJITCS)"},{"key":"285_CR15","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","volume":"73","author":"G Haixiang","year":"2017","unstructured":"Haixiang, G., Yijing, Li., Jennifer Shang, Gu., Mingyun, H.Y., Bing, G.: Learning from class-imbalanced data: review of methods and applications. Expert Syst. Appl. 73, 220\u2013239 (2017)","journal-title":"Expert Syst. Appl."},{"key":"285_CR16","doi-asserted-by":"publisher","unstructured":"Halimu, C., Kasem, A., Shah Newaz, S. H.: Empirical Comparison of Area under ROC curve (AUC) and Mathew Correlation Coefficient (MCC) for Evaluating Machine Learning Algorithms on Imbalanced Datasets for Binary Classification. In: Proceedings of the 3rd International Conference on Machine Learning and Soft Computing (ICMLSC 2019). Association for Computing Machinery, New York, NY, USA, 1\u20136 (2019) https:\/\/doi.org\/10.1145\/3310986.3311023","DOI":"10.1145\/3310986.3311023"},{"key":"285_CR17","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"J Hanley","year":"1982","unstructured":"Hanley, J., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic ROC curve. Radiology 143, 29\u201336 (1982)","journal-title":"Radiology"},{"key":"285_CR18","doi-asserted-by":"publisher","first-page":"24184","DOI":"10.1109\/access.2018.2817572","volume":"6","author":"S Huda","year":"2018","unstructured":"Huda, S., Liu, K., Abdelrazek, M., Ibrahim, A., Alyahya, S., Al-Dossari, H., Ahmad, S.: An ensemble oversampling model for class imbalance problem in software defect prediction. IEEE Access 6, 24184\u201324195 (2018). https:\/\/doi.org\/10.1109\/access.2018.2817572","journal-title":"IEEE Access"},{"key":"285_CR19","first-page":"448","volume":"6","author":"T Ivan","year":"1976","unstructured":"Ivan, T.: An experiment with the edited nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. 6, 448\u2013452 (1976)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"5","key":"285_CR20","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1080\/08839514.2019.1577017","volume":"33","author":"P Kaur","year":"2019","unstructured":"Kaur, P., Gossain, A.: FF-SMOTE: a metaheuristic approach to combat class imbalance in binary classification. J. Appl. Artif. Intell. 33(5), 420\u2013439 (2019)","journal-title":"J. Appl. Artif. Intell."},{"key":"285_CR21","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jss.2017.04.016","volume":"137","author":"L Kumar","year":"2018","unstructured":"Kumar, L., Sripada, S.K., Sureka, A., Rath, S.K.: Effective fault prediction model developed using least square support vector machine (LSSVM). J. Syst. Softw. 137, 686\u2013712 (2018)","journal-title":"J. Syst. Softw."},{"key":"285_CR22","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.infsof.2014.07.005","volume":"58","author":"IH Laradji","year":"2015","unstructured":"Laradji, I.H., Alshayeb, M., Ghouti, L.: Software defect prediction using ensemble learning on selected features. Inf. Softw. Technol. 58, 388\u2013402 (2015)","journal-title":"Inf. Softw. Technol."},{"key":"285_CR23","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.eswa.2018.01.008","volume":"98","author":"HK Lee","year":"2018","unstructured":"Lee, H.K., Kim, S.B.: An overlap-sensitive margin classifier for imbalanced and overlapping data. Expert Syst. Appl. 98, 72\u201383 (2018)","journal-title":"Expert Syst. Appl."},{"key":"285_CR24","volume-title":"Testing statistical hypothesis: springer texts in statistics","author":"EL Lehmann","year":"2008","unstructured":"Lehmann, E.L., Romano, J.P.: Testing statistical hypothesis: springer texts in statistics. Springer, New York (2008)"},{"key":"285_CR25","doi-asserted-by":"publisher","first-page":"2473","DOI":"10.3233\/IFS-141220,IOSPress","volume":"27","author":"Y Ma","year":"2014","unstructured":"Ma, Y., Pan, W., Zhu, S., Yin, H., Luo, J.: An improved semi-supervised learning method for software defect prediction. J. Intell. Fuzzy Syst. 27, 2473\u20132480 (2014). https:\/\/doi.org\/10.3233\/IFS-141220,IOSPress","journal-title":"J. Intell. Fuzzy Syst."},{"key":"285_CR26","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1016\/j.asoc.2014.11.023","volume":"27","author":"R Malhotra","year":"2015","unstructured":"Malhotra, R.: A systematic review of machine learning techniques for software fault prediction. Appl. Soft Comput. 27, 504\u2013518 (2015)","journal-title":"Appl. Soft Comput."},{"issue":"28","key":"285_CR27","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.neucom.2018.04.090","volume":"343","author":"R Malhotra","year":"2019","unstructured":"Malhotra, R., Kamal, S.: An empirical study to investigate oversampling methods for improving software defect prediction using imbalanced data. Neurocomputing 343(28), 120\u2013140 (2019). https:\/\/doi.org\/10.1016\/j.neucom.2018.04.090","journal-title":"Neurocomputing"},{"issue":"11","key":"285_CR28","first-page":"1","volume":"32","author":"T Menzies","year":"2007","unstructured":"Menzies, T., DiStefano, J., Orrego, A., Chapman, R.: Data mining static code attributes to learn defect predictors. IEEE Trans. Softw. Eng. 32(11), 1\u201312 (2007)","journal-title":"IEEE Trans. Softw. Eng."},{"key":"285_CR29","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.ins.2018.02.027","volume":"441","author":"DG Miholca","year":"2018","unstructured":"Miholca, D.G., Czibula, I., Czibula, A.: novel approach for software defect prediction through hybridizing gradual relational association rules with artificial neural networks. J. Inf. Sci. 441, 152\u2013170 (2018)","journal-title":"J. Inf. Sci."},{"key":"285_CR30","volume-title":"Machine Learning","author":"T Mitchell","year":"1997","unstructured":"Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)"},{"key":"285_CR31","unstructured":"NASA (2015). https:\/\/www.nasa.gov\/sites\/default\/files\/files\/Space_Math_VI_2015.pdf."},{"key":"285_CR32","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.jss.2018.06.025","volume":"144","author":"R Ozak\u0131nc\u0131","year":"2018","unstructured":"Ozak\u0131nc\u0131, R., Tarhan, A.: Early software defect prediction: \u00a8a systematic map and review. J. Syst. Softw. 144, 216\u2013239 (2018). https:\/\/doi.org\/10.1016\/j.jss.2018.06.025","journal-title":"J. Syst. Softw."},{"key":"285_CR33","unstructured":"PROMISE. http:\/\/promise.site.uottawa.ca\/SERepository."},{"key":"285_CR34","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s12530-018-9261-9","volume":"11","author":"KN Rao","year":"2020","unstructured":"Rao, K.N., Reddy, C.S.: A novel under sampling strategy for efficient software defect analysis of skewed distributed data. Evol. Syst. 11, 119\u2013131 (2020). https:\/\/doi.org\/10.1007\/s12530-018-9261-9","journal-title":"Evol. Syst."},{"key":"285_CR35","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.eswa.2017.04.014","volume":"82","author":"S Rathore","year":"2017","unstructured":"Rathore, S., Kumar, S.: Towards an ensemble-based system for predicting the number of software faults. Expert Syst. Appl. 82, 357\u2013382 (2017a)","journal-title":"Expert Syst. Appl."},{"key":"285_CR36","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.knosys.2016.12.017","volume":"119","author":"SS Rathore","year":"2017","unstructured":"Rathore, S.S., Kumar, S.: Linear and non-linear heterogeneous ensemble methods to predict the number of faults in software systems. Knowl. Based Syst. 119, 232\u2013256 (2017b)","journal-title":"Knowl. Based Syst."},{"issue":"2","key":"285_CR37","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s10462-017-9563-5","volume":"51","author":"SS Rathore","year":"2019","unstructured":"Rathore, S.S., Kumar, S.: A study on software fault prediction techniques. Artif. Intell. Rev. 51(2), 255\u2013327 (2019). https:\/\/doi.org\/10.1007\/s10462-017-9563-5","journal-title":"Artif. Intell. Rev."},{"key":"285_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","volume":"33","author":"L Rokach","year":"2010","unstructured":"Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33, 1\u201339 (2010)","journal-title":"Artif. Intell. Rev."},{"key":"285_CR39","volume-title":"Probability and Statistics for Engineers and Scientists","author":"SM Ross","year":"2005","unstructured":"Ross, S.M.: Probability and Statistics for Engineers and Scientists, 3rd edn. Elsevier Press, Amsterdam (2005)","edition":"3"},{"key":"285_CR40","unstructured":"Sayyad, S., Menzies, T.: The PROMISE Repository of Software Engineering Databases. Canada: University of Ottawa, http:\/\/promise.site.uottawa.ca\/ SERepository (2015)"},{"key":"285_CR41","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.is.2015.02.006","volume":"51","author":"MJ Siers","year":"2015","unstructured":"Siers, M.J., Islam, M.Z.: Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem. Inf. Syst. 51, 62\u201371 (2015)","journal-title":"Inf. Syst."},{"key":"285_CR42","doi-asserted-by":"publisher","DOI":"10.3390\/sym11020212","author":"LH Son","year":"2019","unstructured":"Son, L.H., Pritam, N., Khari, M., Kumar, R., Phuong, P.T.M., Thong, P.H.: Empirical study of software defect prediction: a systematic mapping. Symmetry (2019). https:\/\/doi.org\/10.3390\/sym11020212","journal-title":"Symmetry"},{"key":"285_CR43","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2018.2836442","author":"Q Song","year":"2018","unstructured":"Song, Q., Guo, Y., Shepperd, M.: A comprehensive investigation of the role of imbalanced learning for software defect prediction. IEEE Trans. Softw. Eng. (2018). https:\/\/doi.org\/10.1109\/TSE.2018.2836442","journal-title":"IEEE Trans. Softw. Eng."},{"issue":"11","key":"285_CR44","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/TSE.2018.2876537","volume":"46","author":"C Tantithamthavorn","year":"2018","unstructured":"Tantithamthavorn, C., Hassan, A.E., Matsumoto, K.: The impact of class rebalancing techniques on the performance and interpretation of defect prediction models. IEEE Trans. Softw. Eng. 46(11), 1200\u20131219 (2018a)","journal-title":"IEEE Trans. Softw. Eng."},{"issue":"7","key":"285_CR45","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1109\/TSE.2018.2794977","volume":"45","author":"C Tantithamthavorn","year":"2018","unstructured":"Tantithamthavorn, C., McIntosh, S., Hassan, A.E., Matsumoto, K.: The impact of automated parameter optimization on defect prediction models. IEEE Trans. Softw. Eng. 45(7), 683\u2013711 (2018b)","journal-title":"IEEE Trans. Softw. Eng."},{"issue":"4","key":"285_CR46","first-page":"308","volume":"2","author":"J Thomas","year":"1976","unstructured":"Thomas, J.: McCabe, a complexity measure. IEEE Trans. Softw. Eng. 2(4), 308\u2013320 (1976)","journal-title":"IEEE Trans. Softw. Eng."},{"key":"285_CR47","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.infsof.2017.11.008","volume":"96","author":"H Tong","year":"2018","unstructured":"Tong, H., Liu, B., Wang, S.: Software defect prediction using stacked denoising autoencoders and two-stage ensemble learning. Inf. Softw. Technol. 96, 94\u2013111 (2018)","journal-title":"Inf. Softw. Technol."},{"key":"285_CR48","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ins.2018.10.029","volume":"477","author":"CF Tsai","year":"2019","unstructured":"Tsai, C.F., Lin, W.C., Hu, Y.H., Yao, G.T.: Under-sampling class imbalanced datasets by combining clustering analysis and instance selection. Inf. Sci. 477, 47\u201354 (2019)","journal-title":"Inf. Sci."},{"key":"285_CR49","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ins.2019.08.062","volume":"509","author":"P Vuttipittayamongkol","year":"2020","unstructured":"Vuttipittayamongkol, P., Elyan, E.: Neighbourhood-based undersampling approach for handling imbalanced and overlapped data. Inf. Sci. 509, 47\u201370 (2020). https:\/\/doi.org\/10.1016\/j.ins.2019.08.062","journal-title":"Inf. Sci."},{"issue":"2","key":"285_CR50","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1109\/TR.2013.2259203","volume":"62","author":"S Wang","year":"2013","unstructured":"Wang, S., Yao, X.: Using class imbalance learning for software defect prediction. IEEE Trans. Reliab. 62(2), 434\u2013443 (2013)","journal-title":"IEEE Trans. Reliab."},{"key":"285_CR51","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1007\/s10515-015-0179-1","volume":"23","author":"T Wang","year":"2015","unstructured":"Wang, T., Zhang, Z., Jing, X., Zhang, L.: Multiple kernel ensemble learning for software defect prediction. Autom. Softw. Eng. 23, 569\u2013590 (2015)","journal-title":"Autom. Softw. Eng."},{"issue":"2","key":"285_CR52","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241\u2013259 (1992)","journal-title":"Neural Netw."},{"key":"285_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","volume":"14","author":"XD Wu","year":"2007","unstructured":"Wu, X.D., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14, 1\u201337 (2007). https:\/\/doi.org\/10.1007\/s10115-007-0114-2","journal-title":"Knowl. Inf. Syst."},{"key":"285_CR54","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.infsof.2014.12.006","volume":"61","author":"X Xia","year":"2015","unstructured":"Xia, X., Lo, D., Shihab, E., Wang, X., Yang, X.: ELBlocker: predicting blocking bugs with ensemble imbalance learning. Inf. Softw. Technol. 61, 93\u2013106 (2015)","journal-title":"Inf. Softw. Technol."},{"key":"285_CR55","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.infsof.2017.03.007","volume":"87","author":"X Yang","year":"2017","unstructured":"Yang, X., Lo, D., Xia, X., Sun, J.: TLEL: a two-layer ensemble learning approach for just-in-time defect prediction. J. Inf. Softw. Technol. 87, 206\u2013220 (2017)","journal-title":"J. Inf. Softw. Technol."},{"issue":"2","key":"285_CR56","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/s11704-017-6015-y","volume":"12","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Lo, D., Xia, X., Sun, J.: Combined classifier for cross-project defect prediction: an extended empirical study. Front. Comput. Sci. 12(2), 280\u2013296 (2018)","journal-title":"Front. Comput. Sci."}],"container-title":["Automated Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-021-00285-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10515-021-00285-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-021-00285-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T01:56:33Z","timestamp":1699322193000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10515-021-00285-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,7]]},"references-count":56,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["285"],"URL":"https:\/\/doi.org\/10.1007\/s10515-021-00285-y","relation":{},"ISSN":["0928-8910","1573-7535"],"issn-type":[{"value":"0928-8910","type":"print"},{"value":"1573-7535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,7]]},"assertion":[{"value":"3 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"14"}}