{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:31:56Z","timestamp":1768415516567,"version":"3.49.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T00:00:00Z","timestamp":1718409600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T00:00:00Z","timestamp":1718409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100000038","name":"NSERC","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Empir Software Eng"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s10664-024-10492-2","type":"journal-article","created":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T10:01:41Z","timestamp":1718445701000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Post deployment recycling of machine learning models"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1399-0747","authenticated-orcid":false,"given":"Harsh","family":"Patel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bram","family":"Adams","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed E.","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,15]]},"reference":[{"issue":"1","key":"10492_CR1","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/TNNLS.2013.2251352","volume":"25","author":"D Brzezinski","year":"2013","unstructured":"Brzezinski D, Stefanowski J (2013) Reacting to different types of concept drift: the accuracy updated ensemble algorithm. IEEE Transactions on Neural Networks and Learning Systems 25(1):81\u201394","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"3","key":"10492_CR2","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1109\/TSE.2022.3175789","volume":"49","author":"GG Cabral","year":"2022","unstructured":"Cabral GG, Minku LL (2022) Towards reliable online just-in-time software defect prediction. IEEE Transactions on Software Engineering 49(3):1342\u20131358","journal-title":"IEEE Transactions on Software Engineering"},{"key":"10492_CR3","doi-asserted-by":"crossref","unstructured":"Cabral GG, Minku LL, Shihab E, Mujahid S (2019) Class imbalance evolution and verification latency in just-in-time software defect prediction. In: 2019 IEEE\/ACM 41st International Conference on Software Engineering (ICSE), pp 666\u2013676. IEEE","DOI":"10.1109\/ICSE.2019.00076"},{"key":"10492_CR4","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16:321\u2013357","journal-title":"Journal of Artificial Intelligence Research"},{"key":"10492_CR5","unstructured":"Chen L, Zaharia M, Zou J (2023) Frugalgpt: How to use large language models while reducing cost and improving performance. arXiv:2305.05176"},{"key":"10492_CR6","doi-asserted-by":"crossref","unstructured":"Chen Z, Liu B (2018) Lifelong machine learning, vol 1. Springer","DOI":"10.1007\/978-3-031-01581-6_1"},{"key":"10492_CR7","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.compind.2022.103764","volume":"143","author":"YJ Cruz","year":"2022","unstructured":"Cruz YJ, Rivas M, Quiza R, Haber RE, Casta\u00f1o F, Villalonga A (2022) A two-step machine learning approach for dynamic model selection: a case study on a micro milling process. Computers in Industry 143:103\u2013764","journal-title":"Computers in Industry"},{"issue":"7","key":"10492_CR8","first-page":"3366","volume":"44","author":"M De Lange","year":"2021","unstructured":"De Lange M, Aljundi R, Masana M, Parisot S, Jia X, Leonardis A, Slabaugh G, Tuytelaars T (2021) A continual learning survey: defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(7):3366\u20133385","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10492_CR9","unstructured":"Diethe T, Borchert T, Thereska E, Balle B, Lawrence N (2019) Continual learning in practice. arXiv:1903.05202"},{"key":"10492_CR10","doi-asserted-by":"crossref","unstructured":"Ekanayake J, Tappolet J, Gall HC, Bernstein A (2009) Tracking concept drift of software projects using defect prediction quality. In: 2009 6th IEEE international working conference on mining software repositories, pp 51\u201360. IEEE","DOI":"10.1109\/MSR.2009.5069480"},{"issue":"10","key":"10492_CR11","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1109\/TNN.2011.2160459","volume":"22","author":"R Elwell","year":"2011","unstructured":"Elwell R, Polikar R (2011) Incremental learning of concept drift in nonstationary environments. IEEE Transactions on Neural Networks 22(10):1517\u20131531","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"1","key":"10492_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3467895","volume":"31","author":"D Falessi","year":"2011","unstructured":"Falessi D, Ahluwalia A, Penta MD (2011) The impact of dormant defects on defect prediction: a study of 19 apache projects. ACM Transactions on Software Engineering and Methodology (TOSEM) 31(1):1\u201326","journal-title":"ACM Transactions on Software Engineering and Methodology (TOSEM)"},{"key":"10492_CR13","doi-asserted-by":"crossref","unstructured":"Falleri JR, Morandat F, Blanc X, Martinez M, Monperrus M (2014) Fine-grained and accurate source code differencing. In: Proceedings of the 29th ACM\/IEEE international conference on automated software engineering, pp 313\u2013324","DOI":"10.1145\/2642937.2642982"},{"key":"10492_CR14","doi-asserted-by":"crossref","unstructured":"Forman G (2006) Tackling concept drift by temporal inductive transfer. In: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval, pp 252\u2013259","DOI":"10.1145\/1148170.1148216"},{"key":"10492_CR15","doi-asserted-by":"crossref","unstructured":"Gao S, Zhang H, Gao C, Wang C (2023) Keeping pace with ever-increasing data: towards continual learning of code intelligence models. arXiv:2302.03482","DOI":"10.1109\/ICSE48619.2023.00015"},{"issue":"2","key":"10492_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2543581.2543595","volume":"46","author":"I Herraiz","year":"2013","unstructured":"Herraiz I, Rodriguez D, Robles G, Gonzalez-Barahona JM (2013) The evolution of the laws of software evolution: a discussion based on a systematic literature review. ACM Computing Surveys (CSUR) 46(2):1\u201328","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"10492_CR17","unstructured":"Hess MR, Kromrey JD (2004) Robust confidence intervals for effect sizes: a comparative study of cohen\u2019sd and cliff\u2019s under non-normality and heterogeneous variances. In: Annual meeting of the American educational research association, vol 1. Citeseer"},{"key":"10492_CR18","doi-asserted-by":"crossref","unstructured":"Hoang T, Dam HK, Kamei Y, Lo D, Ubayashi N (2019) Deepjit: an end-to-end deep learning framework for just-in-time defect prediction. In: 2019 IEEE\/ACM 16th international conference on Mining Software Repositories (MSR), pp 34\u201345. IEEE","DOI":"10.1109\/MSR.2019.00016"},{"key":"10492_CR19","doi-asserted-by":"crossref","unstructured":"James G, Witten D, Hastie T, Tibshirani R et al (2013) An introduction to statistical learning, vol 112. Springer","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"10492_CR20","doi-asserted-by":"crossref","unstructured":"Jiang T, Tan L, Kim S (2013) Personalized defect prediction. In: 2013 28th IEEE\/ACM international conference on Automated Software Engineering (ASE), pp 279\u2013289. Ieee","DOI":"10.1109\/ASE.2013.6693087"},{"key":"10492_CR21","doi-asserted-by":"publisher","first-page":"2072","DOI":"10.1007\/s10664-015-9400-x","volume":"21","author":"Y Kamei","year":"2016","unstructured":"Kamei Y, Fukushima T, McIntosh S, Yamashita K, Ubayashi N, Hassan AE (2016) Studying just-in-time defect prediction using cross-project models. Empirical Software Engineering 21:2072\u20132106","journal-title":"Empirical Software Engineering"},{"issue":"6","key":"10492_CR22","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1109\/TSE.2012.70","volume":"39","author":"Y Kamei","year":"2012","unstructured":"Kamei Y, Shihab E, Adams B, Hassan AE, Mockus A, Sinha A, Ubayashi N (2012) A large-scale empirical study of just-in-time quality assurance. IEEE Transactions on Software Engineering 39(6):757\u2013773","journal-title":"IEEE Transactions on Software Engineering"},{"key":"10492_CR23","doi-asserted-by":"crossref","unstructured":"Keshavarz H, Nagappan M (2022) Apachejit: a large dataset for just-in-time defect prediction. In: Proceedings of the 19th international conference on mining software repositories, pp 191\u2013195","DOI":"10.1145\/3524842.3527996"},{"issue":"2","key":"10492_CR24","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1109\/TSE.2007.70773","volume":"34","author":"S Kim","year":"2008","unstructured":"Kim S, Whitehead EJ, Zhang Y (2008) Classifying software changes: clean or buggy? IEEE Transactions on Software Engineering 34(2):181\u2013196","journal-title":"IEEE Transactions on Software Engineering"},{"key":"10492_CR25","doi-asserted-by":"crossref","unstructured":"Kononenko O, Baysal O, Guerrouj L, Cao Y, Godfrey MW (2015) Investigating code review quality: Do people and participation matter? In: 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME), pp 111\u2013120. IEEE","DOI":"10.1109\/ICSM.2015.7332457"},{"key":"10492_CR26","unstructured":"Kubat M, Matwin S et al (1997) Addressing the curse of imbalanced training sets: one-sided selection. In: Icml, vol 97, p 179. Citeseer"},{"key":"10492_CR27","unstructured":"Lema\u00eetre G, Nogueira F, Aridas CK (2017) Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. Journal of Machine Learning Research 18(17):1\u20135. http:\/\/jmlr.org\/papers\/v18\/16-365"},{"key":"10492_CR28","unstructured":"Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Advances in neural information processing systems 30"},{"issue":"2","key":"10492_CR29","doi-asserted-by":"publisher","first-page":"545","DOI":"10.11144\/Javeriana.upsy10-2.cdcp","volume":"10","author":"G Macbeth","year":"2011","unstructured":"Macbeth G, Razumiejczyk E, Ledesma RD (2011) Cliff\u2019s delta calculator: a non-parametric effect size program for two groups of observations. Universitas Psychologica 10(2):545\u2013555","journal-title":"Universitas Psychologica"},{"key":"10492_CR30","doi-asserted-by":"crossref","unstructured":"McIntosh S, Kamei Y (2018) Are fix-inducing changes a moving target? a longitudinal case study of just-in-time defect prediction. In: Proceedings of the 40th international conference on software engineering, pp 560\u2013560","DOI":"10.1145\/3180155.3182514"},{"issue":"2","key":"10492_CR31","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1002\/bltj.2229","volume":"5","author":"A Mockus","year":"2000","unstructured":"Mockus A, Weiss DM (2000) Predicting risk of software changes. Bell Labs Technical Journal 5(2):169\u2013180","journal-title":"Bell Labs Technical Journal"},{"key":"10492_CR32","doi-asserted-by":"crossref","unstructured":"Olewicki D, Habchi S, Nayrolles M, Faramarzi M, Chandar S, Adams B (2023) Towards lifelong learning for software analytics models: empirical study on brown build and risk prediction. arXiv:2305.09824","DOI":"10.1145\/3639477.3639717"},{"key":"10492_CR33","doi-asserted-by":"crossref","unstructured":"Olewicki D, Nayrolles M, Adams B (2022) Towards language-independent brown build detection. In: Proceedings of the 44th International Conference on Software Engineering, pp 2177\u20132188","DOI":"10.1145\/3510003.3510122"},{"issue":"6","key":"10492_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3533378","volume":"55","author":"A Paleyes","year":"2022","unstructured":"Paleyes A, Urma RG, Lawrence ND (2022) Challenges in deploying machine learning: a survey of case studies. ACM Computing Surveys 55(6):1\u201329","journal-title":"ACM Computing Surveys"},{"key":"10492_CR35","unstructured":"Patel H (2023) Post-deployment model recycling. https:\/\/github.com\/SAILResearch\/replication-23-harsh-model_recycling"},{"key":"10492_CR36","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. Journal of Machine Learning Research 12:2825\u20132830","journal-title":"Journal of Machine Learning Research"},{"key":"10492_CR37","doi-asserted-by":"crossref","unstructured":"Polikar R, Upda L, Upda SS, Honavar V (2001) Learn++: an incremental learning algorithm for supervised neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 31(4):497\u2013508","DOI":"10.1109\/5326.983933"},{"key":"10492_CR38","doi-asserted-by":"crossref","unstructured":"Pornprasit C, Tantithamthavorn CK (2021) Jitline: a simpler, better, faster, finer-grained just-in-time defect prediction. In: 2021 IEEE\/ACM 18th international conference on Mining Software Repositories (MSR), pp 369\u2013379. IEEE","DOI":"10.1109\/MSR52588.2021.00049"},{"key":"10492_CR39","doi-asserted-by":"crossref","unstructured":"Quinonero-Candela J, Sugiyama M, Schwaighofer A, Lawrence ND (2008) Dataset shift in machine learning. Mit Press","DOI":"10.7551\/mitpress\/9780262170055.001.0001"},{"issue":"7","key":"10492_CR40","doi-asserted-by":"publisher","first-page":"2245","DOI":"10.1109\/TSE.2021.3056941","volume":"48","author":"GK Rajbahadur","year":"2021","unstructured":"Rajbahadur GK, Wang S, Oliva GA, Kamei Y, Hassan AE (2021) The impact of feature importance methods on the interpretation of defect classifiers. IEEE Transactions on Software Engineering 48(7):2245\u20132261","journal-title":"IEEE Transactions on Software Engineering"},{"issue":"24","key":"10492_CR41","doi-asserted-by":"publisher","first-page":"638","DOI":"10.21105\/joss.00638","volume":"3","author":"S Raschka","year":"2018","unstructured":"Raschka S (2018) Mlxtend: providing machine learning and data science utilities and extensions to python\u2019s scientific computing stack. Journal of open source software 3(24):638","journal-title":"Journal of open source software"},{"key":"10492_CR42","unstructured":"Schelter S, Biessmann F, Januschowski T, Salinas D, Seufert S, Szarvas G (2015) On challenges in machine learning model management"},{"key":"10492_CR43","doi-asserted-by":"crossref","unstructured":"Song L, Minku L, Yao X (2023) On the validity of retrospective predictive performance evaluation procedures in just-in-time software defect prediction. Empirical Software Engineering","DOI":"10.1007\/s10664-023-10341-8"},{"key":"10492_CR44","doi-asserted-by":"crossref","unstructured":"Street WN, Kim Y (2001) A streaming ensemble algorithm (sea) for large-scale classification. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, pp 377\u2013382","DOI":"10.1145\/502512.502568"},{"key":"10492_CR45","doi-asserted-by":"crossref","unstructured":"Strubell E, Ganesh A, McCallum A (2019) Energy and policy considerations for deep learning in nlp. arXiv:1906.02243","DOI":"10.18653\/v1\/P19-1355"},{"issue":"10","key":"10492_CR46","doi-asserted-by":"publisher","first-page":"4822","DOI":"10.1109\/TNNLS.2017.2775225","volume":"29","author":"Y Sun","year":"2018","unstructured":"Sun Y, Tang K, Zhu Z, Yao X (2018) Concept drift adaptation by exploiting historical knowledge. IEEE Transactions on Neural Networks and Learning Systems 29(10):4822\u20134832","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"1","key":"10492_CR47","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1109\/TSE.2022.3150153","volume":"49","author":"S Tabassum","year":"2022","unstructured":"Tabassum S, Minku LL, Feng D (2022) Cross-project online just-in-time software defect prediction. IEEE Transactions on Software Engineering 49(1):268\u2013287","journal-title":"IEEE Transactions on Software Engineering"},{"key":"10492_CR48","doi-asserted-by":"crossref","unstructured":"Tan M, Tan L, Dara S, Mayeux C (2015) Online defect prediction for imbalanced data. In: 2015 IEEE\/ACM 37th IEEE international conference on software engineering, vol 2, pp 99\u2013108. IEEE","DOI":"10.1109\/ICSE.2015.139"},{"issue":"7","key":"10492_CR49","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 AE, Matsumoto K (2018) The impact of automated parameter optimization on defect prediction models. IEEE Transactions on Software Engineering 45(7):683\u2013711","journal-title":"IEEE Transactions on Software Engineering"},{"issue":"1","key":"10492_CR50","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.inffus.2006.11.002","volume":"9","author":"A Tsymbal","year":"2008","unstructured":"Tsymbal A, Pechenizkiy M, Cunningham P, Puuronen S (2008) Dynamic integration of classifiers for handling concept drift. Information fusion 9(1):56\u201368","journal-title":"Information fusion"},{"key":"10492_CR51","unstructured":"Weisstein EW (2004) Bonferroni correction. https:\/\/mathworld.wolfram.com\/"},{"issue":"2","key":"10492_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 DH (1992) Stacked generalization. Neural Networks 5(2):241\u2013259","journal-title":"Neural Networks"},{"key":"10492_CR53","doi-asserted-by":"crossref","unstructured":"Woolson RF (2007) Wilcoxon signed-rank test. Wiley encyclopedia of clinical trials pp 1\u20133","DOI":"10.1002\/9780471462422.eoct979"},{"key":"10492_CR54","doi-asserted-by":"crossref","unstructured":"Zeng Z, Zhang Y, Zhang H, Zhang L (2021) Deep just-in-time defect prediction: How far are we? In: Proceedings of the 30th ACM SIGSOFT international symposium on software testing and analysis, pp 427\u2013438","DOI":"10.1145\/3460319.3464819"},{"issue":"10","key":"10492_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3567550","volume":"55","author":"Y Zhao","year":"2023","unstructured":"Zhao Y, Damevski K, Chen H (2023) A systematic survey of just-in-time software defect prediction. ACM Computing Surveys 55(10):1\u201335","journal-title":"ACM Computing Surveys"}],"container-title":["Empirical Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-024-10492-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10664-024-10492-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-024-10492-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T15:26:56Z","timestamp":1720193216000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10664-024-10492-2"}},"subtitle":["Don\u2019t Throw Away Your Old Models!"],"short-title":[],"issued":{"date-parts":[[2024,6,15]]},"references-count":55,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["10492"],"URL":"https:\/\/doi.org\/10.1007\/s10664-024-10492-2","relation":{},"ISSN":["1382-3256","1573-7616"],"issn-type":[{"value":"1382-3256","type":"print"},{"value":"1573-7616","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,15]]},"assertion":[{"value":"24 April 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 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":"All authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}],"article-number":"100"}}