{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:17:28Z","timestamp":1740107848699,"version":"3.37.3"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"21-22","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s00500-024-10324-x","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T03:17:30Z","timestamp":1732504650000},"page":"12897-12916","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Software defect density prediction using grey system theory and fuzzy logic"],"prefix":"10.1007","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0323-6452","authenticated-orcid":false,"given":"Mohammad","family":"Azzeh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yousef","family":"Elsheikh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yousef","family":"Alqasrawi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"key":"10324_CR1","doi-asserted-by":"publisher","unstructured":"Azzeh M, Neagu D, Cowling PI (2009) Fuzzy grey relational analysis for software effort estimation, Empir Softw Eng 151, 15(1): 60\u201390, https:\/\/doi.org\/10.1007\/S10664-009-9113-0.","DOI":"10.1007\/S10664-009-9113-0"},{"key":"10324_CR2","doi-asserted-by":"publisher","unstructured":"Azzeh M, Neagu D, Cowling P Software effort estimation based on weighted fuzzy grey relational analysis, In: Proceedings of the 5th International Conference on Predictor Models in Software Engineering, 2009, pp. 1\u201310. https:\/\/doi.org\/10.1145\/1540438.1540450.","DOI":"10.1145\/1540438.1540450"},{"key":"10324_CR3","doi-asserted-by":"publisher","unstructured":"Azzeh M, Neagu D, Cowling PI (2010) Fuzzy grey relational analysis for software effort estimation, Empir Softw Eng 15(1), https:\/\/doi.org\/10.1007\/s10664-009-9113-0.","DOI":"10.1007\/s10664-009-9113-0"},{"key":"10324_CR4","doi-asserted-by":"publisher","unstructured":"Azzeh M, Neagu D, Cowling PI (2011) Analogy-based software effort estimation using Fuzzy numbers, J Syst Softw 84(2), https:\/\/doi.org\/10.1016\/j.jss.2010.09.028.","DOI":"10.1016\/j.jss.2010.09.028"},{"key":"10324_CR5","doi-asserted-by":"publisher","unstructured":"Azzeh M, Nassif ABAB, Banitaan S (2016) An application of classification and class decomposition to use case point estimation method. https:\/\/doi.org\/10.1109\/ICMLA.2015.105.","DOI":"10.1109\/ICMLA.2015.105"},{"key":"10324_CR6","doi-asserted-by":"publisher","unstructured":"Calikli G, Bener A (2013) An algorithmic approach to missing data problem in modeling human aspects in software development. In: ACM International Conference Proceeding Series, vol. Part F1288, pp. 1\u201310. https:\/\/doi.org\/10.1145\/2499393.2499394.","DOI":"10.1145\/2499393.2499394"},{"key":"10324_CR7","doi-asserted-by":"publisher","unstructured":"D\u2019Ambros M, Lanza M, Robbes R (2011) Evaluating defect prediction approaches: a benchmark and an extensive comparison, Empir Softw Eng 174, 17(4): 531\u2013577, https:\/\/doi.org\/10.1007\/S10664-011-9173-9.","DOI":"10.1007\/S10664-011-9173-9"},{"key":"10324_CR8","doi-asserted-by":"crossref","unstructured":"Gao X-Y (2023) Oceanic shallow-water investigations on a generalized Whitham\u2013Broer\u2013Kaup\u2013Boussinesq\u2013Kupershmidt system. Phys Fluids 35(12).","DOI":"10.1063\/5.0170506"},{"key":"10324_CR9","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1016\/j.cjph.2023.10.051","volume":"86","author":"X-Y Gao","year":"2023","unstructured":"Gao X-Y (2023b) Considering the wave processes in oceanography, acoustics and hydrodynamics by means of an extended coupled (2+ 1)-dimensional Burgers system. Chin J Phys 86:572\u2013577","journal-title":"Chin J Phys"},{"key":"10324_CR10","doi-asserted-by":"crossref","unstructured":"Gao X-Y Two-layer-liquid and lattice considerations through a (3+ 1)-dimensional generalized Yu-Toda-Sasa-Fukuyama system. Applied Mathematics Letters (2024): 109018.","DOI":"10.1016\/j.aml.2024.109018"},{"key":"10324_CR11","doi-asserted-by":"publisher","unstructured":"Hsu C-J, Huang C-Y Improving effort estimation accuracy by weighted grey relational analysis during software development, 2008, pp 534\u2013541. https:\/\/doi.org\/10.1109\/ASPEC.2007.62.","DOI":"10.1109\/ASPEC.2007.62"},{"key":"10324_CR12","unstructured":"Julong D Introduction to grey system theory, J Grey Syst 1(1): 1\u201324, 1989, Accessed: Feb. 06, 2022. [Online]. http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.678.3477&rep=rep1&type=pdf"},{"key":"10324_CR13","unstructured":"Khalsa SK A Fuzzified approach for the prediction of fault proneness and defect density, 2009."},{"issue":"5","key":"10324_CR14","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1109\/TSE.2007.1001","volume":"33","author":"BA Kitchenham","year":"2007","unstructured":"Kitchenham BA, Mendes E, Travassos GH (2007) Cross versus within-company cost estimation studies: a systematic review. IEEE Trans Software Eng 33(5):316\u2013329. https:\/\/doi.org\/10.1109\/TSE.2007.1001","journal-title":"IEEE Trans Software Eng"},{"key":"10324_CR15","doi-asserted-by":"publisher","unstructured":"Knab P, Pinzger M, Bernstein A (2006) Predicting defect densities in source code files with decision tree learners. In: Proceedings\u2014International Conference on Software Engineering, pp 119\u2013125. https:\/\/doi.org\/10.1145\/1137983.1138012.","DOI":"10.1145\/1137983.1138012"},{"issue":"7","key":"10324_CR16","doi-asserted-by":"publisher","first-page":"1879","DOI":"10.1016\/j.jss.2013.02.053","volume":"86","author":"E Kocaguneli","year":"2013","unstructured":"Kocaguneli E, Menzies T (2013) Software effort models should be assessed via leave-one-out validation. J Syst Softw 86(7):1879\u20131890. https:\/\/doi.org\/10.1016\/j.jss.2013.02.053","journal-title":"J Syst Softw"},{"key":"10324_CR17","unstructured":"Kumar V, Sharma A, Kumar R (2013) Applying soft computing approaches to predict defect density in software product releases: an empirical study, Comput. INFORMATICS, 32(1): 203\u2013224, Accessed: Feb. 04, 2022. [Online]. http:\/\/www.cai2.sk\/ojs\/index.php\/cai\/article\/view\/1472"},{"key":"10324_CR18","doi-asserted-by":"publisher","unstructured":"Kutlubay O, Turhan B, Bener AB (2007) A two-step model for defect density estimation, In: EUROMICRO 2007\u2014Proceedings of the 33rd EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2007, pp 322\u2013329. https:\/\/doi.org\/10.1109\/EUROMICRO.2007.13.","DOI":"10.1109\/EUROMICRO.2007.13"},{"issue":"2","key":"10324_CR19","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/s10515-017-0220-7","volume":"25","author":"Z Li","year":"2018","unstructured":"Li Z, Jing XY, Wu F, Zhu X, Xu B, Ying S (2018) Cost-sensitive transfer kernel canonical correlation analysis for heterogeneous defect prediction. Autom Softw Eng 25(2):201\u2013245. https:\/\/doi.org\/10.1007\/s10515-017-0220-7","journal-title":"Autom Softw Eng"},{"key":"10324_CR20","doi-asserted-by":"publisher","unstructured":"Li Z, Liang P, Li B (2017) Relating alternate modifications to defect density in software development. In: Proceedings - 2017 IEEE\/ACM 39th international conference on software engineering companion, ICSE-C 2017, pp. 308\u2013310. https:\/\/doi.org\/10.1109\/ICSE-C.2017.132.","DOI":"10.1109\/ICSE-C.2017.132"},{"key":"10324_CR21","doi-asserted-by":"publisher","unstructured":"Lopez-Martin C, Azzeh M, Bou-Nassif A, Banitaan S (2019) Upsilon-SVR polynomial kernel for predicting the defect density in new software projects. In: Proceedings\u201417th IEEE international conference on machine learning and applications, ICMLA 2018, pp. 1377\u20131382. https:\/\/doi.org\/10.1109\/ICMLA.2018.00224.","DOI":"10.1109\/ICMLA.2018.00224"},{"key":"10324_CR22","doi-asserted-by":"publisher","unstructured":"L\u00f3pez-Mart\u00edn C, Villuendas-Rey Y, Azzeh M, Bou Nassif A, Banitaan S (2020) Transformed k-nearest neighborhood output distance minimization for predicting the defect density of software projects, J Syst Softw 167: 110592, https:\/\/doi.org\/10.1016\/J.JSS.2020.110592.","DOI":"10.1016\/J.JSS.2020.110592"},{"issue":"3","key":"10324_CR23","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.infsof.2011.09.007","volume":"54","author":"Y Ma","year":"2012","unstructured":"Ma Y, Luo G, Zeng X, Chen A (2012) Transfer learning for cross-company software defect prediction. Inf Softw Technol 54(3):248\u2013256. https:\/\/doi.org\/10.1016\/j.infsof.2011.09.007","journal-title":"Inf Softw Technol"},{"key":"10324_CR24","doi-asserted-by":"publisher","unstructured":"Mandhan N, Verma DK, Kumar S (2015) Analysis of approach for predicting software defect density using static metrics, In: International conference on computing, communication and automation, ICCCA 2015, pp. 880\u2013886. https:\/\/doi.org\/10.1109\/CCAA.2015.7148499.","DOI":"10.1109\/CCAA.2015.7148499"},{"key":"10324_CR25","doi-asserted-by":"publisher","unstructured":"Marchenko A, Abrahamsson P (2007) Predicting software defect density: a case study on automated static code analysis, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 4536 LNCS, 137\u2013140, https:\/\/doi.org\/10.1007\/978-3-540-73101-6_18.","DOI":"10.1007\/978-3-540-73101-6_18"},{"issue":"11","key":"10324_CR26","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1109\/TSE.2006.114","volume":"32","author":"T Menzies","year":"2006","unstructured":"Menzies T, Chen Z, Hihn J, Lum K (2006) Selecting best practices for effort estimation. IEEE Trans Softw Eng 32(11):883\u2013895. https:\/\/doi.org\/10.1109\/TSE.2006.114","journal-title":"IEEE Trans Softw Eng"},{"issue":"4","key":"10324_CR27","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/s10515-010-0069-5","volume":"17","author":"T Menzies","year":"2010","unstructured":"Menzies T, Milton Z, Turhan B, Cukic B, Jiang Y, Bener A (2010) Defect prediction from static code features: Current results, limitations, new approaches. Autom Softw Eng 17(4):375\u2013407. https:\/\/doi.org\/10.1007\/s10515-010-0069-5","journal-title":"Autom Softw Eng"},{"issue":"6","key":"10324_CR28","doi-asserted-by":"publisher","first-page":"822","DOI":"10.1109\/TSE.2012.83","volume":"39","author":"T Menzies","year":"2013","unstructured":"Menzies T et al (2013) Local versus global lessons for defect prediction and effort estimation. IEEE Trans Softw Eng 39(6):822\u2013834. https:\/\/doi.org\/10.1109\/TSE.2012.83","journal-title":"IEEE Trans Softw Eng"},{"key":"10324_CR29","doi-asserted-by":"publisher","unstructured":"Minku LL (2019) A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation, Empir. Softw. Eng. 2019 245, 24(5): 3153\u20133204, https:\/\/doi.org\/10.1007\/S10664-019-09686-W.","DOI":"10.1007\/S10664-019-09686-W"},{"key":"10324_CR30","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1109\/ICSE.2004.1317450","volume":"26","author":"P Mohagheghi","year":"2004","unstructured":"Mohagheghi P, Conradi R, Killi OM, Schwarz H (2004) An empirical study of software reuse vs. defect-density and stability. Proc\u2014Int Conf Softw Eng 26:282\u2013291. https:\/\/doi.org\/10.1109\/ICSE.2004.1317450","journal-title":"Proc\u2014Int Conf Softw Eng"},{"key":"10324_CR31","doi-asserted-by":"publisher","unstructured":"Nagappan N, Ball T Use of relative code churn measures to predict system defect density, Proc.\u201427th Int. Conf. Softw. Eng. ICSE05, pp. 284\u2013292, 2005, https:\/\/doi.org\/10.1145\/1062455.1062514.","DOI":"10.1145\/1062455.1062514"},{"issue":"9","key":"10324_CR32","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1109\/TSE.2017.2720603","volume":"44","author":"J Nam","year":"2018","unstructured":"Nam J, Fu W, Kim S, Menzies T, Tan L (2018) Heterogeneous Defect Prediction. IEEE Trans Softw Eng 44(9):874\u2013896. https:\/\/doi.org\/10.1109\/TSE.2017.2720603","journal-title":"IEEE Trans Softw Eng"},{"key":"10324_CR33","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1007\/978-981-10-7512-4_92","volume":"672","author":"M Padmaja","year":"2018","unstructured":"Padmaja M, Haritha D (2018) Software effort estimation using grey relational analysis with k-means clustering. Adv Intell Syst Comput 672:924\u2013933. https:\/\/doi.org\/10.1007\/978-981-10-7512-4_92","journal-title":"Adv Intell Syst Comput"},{"issue":"4","key":"10324_CR34","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1142\/S0219622009003715","volume":"8","author":"Y Peng","year":"2011","unstructured":"Peng Y, Kou G, Wang G, Wang H, Ko FIS (2011) Empirical evaluation of classifiers for software risk management. Int J Inf Technol Decis Mak 8(4):749\u2013767. https:\/\/doi.org\/10.1142\/S0219622009003715","journal-title":"Int J Inf Technol Decis Mak"},{"issue":"1","key":"10324_CR35","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1142\/S0219622011004282","volume":"10","author":"Y Peng","year":"2012","unstructured":"Peng Y, Kou G, Wang G, Wu W, Shi Y (2012) Ensemble of software defect predictors: an ahp-based evaluation method. Int J Inf Technol Decis Mak 10(1):187\u2013206. https:\/\/doi.org\/10.1142\/S0219622011004282","journal-title":"Int J Inf Technol Decis Mak"},{"key":"10324_CR36","doi-asserted-by":"publisher","unstructured":"Peters F, Menzies T (2012) Privacy and utility for defect prediction: Experiments with MORPH, Proc.\u2014Int. Conf. Softw. Eng., 189\u2013199, https:\/\/doi.org\/10.1109\/ICSE.2012.6227194.","DOI":"10.1109\/ICSE.2012.6227194"},{"key":"10324_CR37","doi-asserted-by":"publisher","unstructured":"Rahmani C, Khazanchi D (2010) A study on defect density of open source software, In: Proceedings - 9th IEEE\/ACIS International Conference on Computer and Information Science, ICIS 2010, 679\u2013683. https:\/\/doi.org\/10.1109\/ICIS.2010.11.","DOI":"10.1109\/ICIS.2010.11"},{"key":"10324_CR38","doi-asserted-by":"publisher","unstructured":"Rathaur S, Kamath N, Ghanekar U (2020) Software defect density prediction based on multiple linear regression, In: Proceedings of the 2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020, pp. 434\u2013439. https:\/\/doi.org\/10.1109\/ICIRCA48905.2020.9183110.","DOI":"10.1109\/ICIRCA48905.2020.9183110"},{"key":"10324_CR39","doi-asserted-by":"crossref","unstructured":"Shen Y et al. (2023) Multi-pole solitons in an inhomogeneous multi-component nonlinear optical medium.Chaos, Solitons & Fractals171: 113497.","DOI":"10.1016\/j.chaos.2023.113497"},{"key":"10324_CR40","doi-asserted-by":"crossref","unstructured":"Shepperd M, S. M.-I. and S. Technology, and U. 2012 Evaluating prediction systems in software project estimation, Inf Softw Technol 54(8): 820\u2013827 2012, Accessed: May 01, 2020. [Online]. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S095058491200002X","DOI":"10.1016\/j.infsof.2011.12.008"},{"key":"10324_CR41","unstructured":"Sherriff M, Williams L, Abstract MV-F Using in-process metrics to predict defect density in haskell programs, 2004. Accessed: Feb. 04, 2022. [Online]. https:\/\/www.academia.edu\/download\/30793445\/ISSRE-FA-SWV04.pdf"},{"key":"10324_CR42","doi-asserted-by":"publisher","unstructured":"SherriffMark, NagappanNachiappan, WilliamsLaurie, and VoukMladen (2005) Early estimation of defect density using an in-process Haskell metrics model, ACM SIGSOFT Softw. Eng. Notes, 30(4): 1\u20136, https:\/\/doi.org\/10.1145\/1082983.1083285","DOI":"10.1145\/1082983.1083285"},{"key":"10324_CR43","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1109\/METRICS.2005.51","volume":"2005","author":"Q Song","year":"2005","unstructured":"Song Q, Shepperd M, Mair C (2005) Using grey relational analysis to predict software effort with small data sets. Proceedings\u2014International Software Metrics Symposium 2005:321\u2013330. https:\/\/doi.org\/10.1109\/METRICS.2005.51","journal-title":"Proceedings\u2014International Software Metrics Symposium"},{"issue":"12","key":"10324_CR44","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1109\/TSE.2018.2836442","volume":"45","author":"Q Song","year":"2019","unstructured":"Song Q, Guo Y, Shepperd M (2019) A comprehensive investigation of the role of imbalanced learning for software defect prediction. IEEE Trans Softw Eng 45(12):1253\u20131269. https:\/\/doi.org\/10.1109\/TSE.2018.2836442","journal-title":"IEEE Trans Softw Eng"},{"key":"10324_CR45","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2018.2876537","author":"C Tantithamthavorn","year":"2018","unstructured":"Tantithamthavorn C, Hassan AE, Matsumoto K (2018) The impact of class rebalancing techniques on the performance and interpretation of defect prediction models. IEEE Trans Softw Eng. https:\/\/doi.org\/10.1109\/TSE.2018.2876537","journal-title":"IEEE Trans Softw Eng"},{"key":"10324_CR46","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 (2018) Software defect prediction using stacked denoising autoencoders and two-stage ensemble learning. Inf Softw Technol 96:94\u2013111. https:\/\/doi.org\/10.1016\/j.infsof.2017.11.008","journal-title":"Inf Softw Technol"},{"issue":"5","key":"10324_CR47","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1007\/s10664-008-9103-7","volume":"14","author":"B Turhan","year":"2009","unstructured":"Turhan B, Menzies T, Bener AB, Di Stefano J (2009) On the relative value of cross-company and within-company data for defect prediction. Empir Softw Eng 14(5):540\u2013578. https:\/\/doi.org\/10.1007\/s10664-008-9103-7","journal-title":"Empir Softw Eng"},{"issue":"1","key":"10324_CR48","doi-asserted-by":"publisher","first-page":"12062","DOI":"10.1088\/1742-6596\/1230\/1\/012062","volume":"1230","author":"D Ulumi","year":"2019","unstructured":"Ulumi D, Series DS (2019) Weighted knn using grey relational analysis for cross-project defect prediction. J Phys Conf Ser 1230(1):12062. https:\/\/doi.org\/10.1088\/1742-6596\/1230\/1\/012062","journal-title":"J Phys Conf Ser"},{"key":"10324_CR49","doi-asserted-by":"publisher","unstructured":"Verma D, Kumar S (2014) An improved approach for reduction of defect density using optimal module sizes, Adv Softw Eng 2014 https:\/\/doi.org\/10.1155\/2014\/803530.","DOI":"10.1155\/2014\/803530"},{"key":"10324_CR50","unstructured":"Verma D, Kumar S (2017) Prediction of defect density for open source software using repository metrics, J Web Eng, Accessed: Feb. 04, 2022. [Online]. https:\/\/journals.riverpublishers.com\/index.php\/JWE\/article\/view\/3289"},{"key":"10324_CR51","doi-asserted-by":"publisher","unstructured":"Wang H, Khoshgoftaar TM, Van Hulse J, Gao K (2011) Metric selection for software defect prediction, 21(2): 237\u2013257, , https:\/\/doi.org\/10.1142\/S0218194011005256.","DOI":"10.1142\/S0218194011005256"},{"issue":"8","key":"10324_CR52","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/34.85677","volume":"13","author":"XL Xie","year":"1991","unstructured":"Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13(8):841\u2013847. https:\/\/doi.org\/10.1109\/34.85677","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10324_CR53","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/J.INFSOF.2015.03.001","volume":"63","author":"HB Yadav","year":"2015","unstructured":"Yadav HB, Yadav DK (2015) A fuzzy logic based approach for phase-wise software defects prediction using software metrics. Inf Softw Technol 63:44\u201357. https:\/\/doi.org\/10.1016\/J.INFSOF.2015.03.001","journal-title":"Inf Softw Technol"},{"issue":"1","key":"10324_CR54","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1109\/TR.2014.2370891","volume":"64","author":"X Yang","year":"2015","unstructured":"Yang X, Tang K, Yao X (2015) A learning-to-rank approach to software defect prediction. IEEE Trans Reliab 64(1):234\u2013246. https:\/\/doi.org\/10.1109\/TR.2014.2370891","journal-title":"IEEE Trans Reliab"},{"issue":"2","key":"10324_CR55","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/S0165-0114(97)00077-8","volume":"90","author":"LA Zadeh","year":"1997","unstructured":"Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111\u2013127. https:\/\/doi.org\/10.1016\/S0165-0114(97)00077-8","journal-title":"Fuzzy Sets Syst"},{"issue":"6","key":"10324_CR56","doi-asserted-by":"publisher","first-page":"4537","DOI":"10.1016\/J.ESWA.2009.12.056","volume":"37","author":"J Zheng","year":"2010","unstructured":"Zheng J (2010) Cost-sensitive boosting neural networks for software defect prediction. Expert Syst Appl 37(6):4537\u20134543. https:\/\/doi.org\/10.1016\/J.ESWA.2009.12.056","journal-title":"Expert Syst Appl"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-024-10324-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-024-10324-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-024-10324-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T07:34:28Z","timestamp":1733902468000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-024-10324-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":56,"journal-issue":{"issue":"21-22","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["10324"],"URL":"https:\/\/doi.org\/10.1007\/s00500-024-10324-x","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"type":"print","value":"1432-7643"},{"type":"electronic","value":"1433-7479"}],"subject":[],"published":{"date-parts":[[2024,11]]},"assertion":[{"value":"17 June 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have not disclosed any competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}