{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T19:21:35Z","timestamp":1778613695994,"version":"3.51.4"},"reference-count":104,"publisher":"Springer Science and Business Media LLC","issue":"39","license":[{"start":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T00:00:00Z","timestamp":1722038400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T00:00:00Z","timestamp":1722038400000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19756-x","type":"journal-article","created":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T04:01:52Z","timestamp":1722052912000},"page":"87237-87298","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A systematic review of transfer learning in software engineering"],"prefix":"10.1007","volume":"83","author":[{"given":"Ruchika","family":"Malhotra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0888-8465","authenticated-orcid":false,"given":"Shweta","family":"Meena","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,27]]},"reference":[{"key":"19756_CR1","unstructured":"Joachims T (1999) Transductive inference for text classification using support vector machines. In Icml 99:200\u2013209. https:\/\/dl.acm.org\/doi\/10.5555\/645528.657646"},{"key":"19756_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big data 3:1\u201340. https:\/\/doi.org\/10.1186\/s40537-016-0043-6","journal-title":"J Big data"},{"key":"19756_CR3","doi-asserted-by":"publisher","first-page":"35179","DOI":"10.1007\/s11042-019-08216-6","volume":"78","author":"P Zhao","year":"2019","unstructured":"Zhao P, Liu Y, Lu Y, Xu B (2019) A sketch recognition method based on transfer deep learning with the fusion of multi-granular sketches. Multimed Tools Appl 78:35179\u201335193. https:\/\/doi.org\/10.1007\/s11042-019-08216-6","journal-title":"Multimed Tools Appl"},{"key":"19756_CR4","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s40537-017-0089-0","volume":"4","author":"O Day","year":"2017","unstructured":"Day O, Khoshgoftaar TM (2017) A survey on heterogeneous transfer learning. J Big Data 4:29. https:\/\/doi.org\/10.1186\/s40537-017-0089-0","journal-title":"J Big Data"},{"key":"19756_CR5","doi-asserted-by":"publisher","first-page":"27473","DOI":"10.1007\/s11042-023-14481-3","volume":"82","author":"I Priyadarshini","year":"2023","unstructured":"Priyadarshini I, Sahu S, Kumar R (2023) A transfer learning approach for detecting offensive and hate speech on social media platforms. Multimed Tools Appl 82:27473\u201327499. https:\/\/doi.org\/10.1007\/s11042-023-14481-3","journal-title":"Multimed Tools Appl"},{"key":"19756_CR6","doi-asserted-by":"publisher","first-page":"12093","DOI":"10.1007\/s11042-021-10833-z","volume":"81","author":"J Chen","year":"2022","unstructured":"Chen J, Sun J, Li Y, Hou C (2022) Object detection in remote sensing images based on deep transfer learning. Multimed Tools Appl 81:12093\u201312109. https:\/\/doi.org\/10.1007\/s11042-021-10833-z","journal-title":"Multimed Tools Appl"},{"key":"19756_CR7","doi-asserted-by":"publisher","first-page":"22355","DOI":"10.1007\/s11042-021-11282-4","volume":"81","author":"J Kang","year":"2022","unstructured":"Kang J, Gwak J (2022) Ensemble of multi-task deep convolutional neural networks using transfer learning for fruit freshness classification. Multimed Tools Appl 81:22355\u201322377. https:\/\/doi.org\/10.1007\/s11042-021-11282-4","journal-title":"Multimed Tools Appl"},{"key":"19756_CR8","doi-asserted-by":"publisher","first-page":"22307","DOI":"10.1007\/s11042-021-11131-4","volume":"81","author":"N Varshney","year":"2022","unstructured":"Varshney N, Bakariya B, Kushwaha AKS (2022) Human activity recognition using deep transfer learning of cross position sensor based on vertical distribution of data. Multimed Tools Appl 81:22307\u201322322. https:\/\/doi.org\/10.1007\/s11042-021-11131-4","journal-title":"Multimed Tools Appl"},{"key":"19756_CR9","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: A Gravitational Search Algorithm. Inf Sci (Ny) 179:2232\u20132248. https:\/\/doi.org\/10.1016\/j.ins.2009.03.004","journal-title":"Inf Sci (Ny)"},{"key":"19756_CR10","doi-asserted-by":"publisher","first-page":"9367","DOI":"10.1007\/s11042-021-11852-6","volume":"81","author":"AH Ornek","year":"2022","unstructured":"Ornek AH, Ceylan M (2022) Medical thermograms\u2019 classification using deep transfer learning models and methods. Multimed Tools Appl 81:9367\u20139384. https:\/\/doi.org\/10.1007\/s11042-021-11852-6","journal-title":"Multimed Tools Appl"},{"key":"19756_CR11","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.1145\/1577069.1755839","volume":"10","author":"ME Taylor","year":"2009","unstructured":"Taylor ME, Stone P (2009) Transfer Learning for Reinforcement Learning Domains\u202f: A Survey. J Mach Learn Res 10:1633\u20131685. https:\/\/doi.org\/10.1145\/1577069.1755839","journal-title":"J Mach Learn Res"},{"key":"19756_CR12","doi-asserted-by":"publisher","first-page":"257","DOI":"10.5626\/jcse.2011.5.3.257","volume":"5","author":"Q Xu","year":"2011","unstructured":"Xu Q, Yang Q (2011) A Survey of Transfer and Multitask Learning in Bioinformatics. J Comput Sci Eng 5:257\u2013268. https:\/\/doi.org\/10.5626\/jcse.2011.5.3.257","journal-title":"J Comput Sci Eng"},{"key":"19756_CR13","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.knosys.2015.01.010","volume":"80","author":"J Lu","year":"2015","unstructured":"Lu J, Behbood V, Hao P et al (2015) Transfer learning using computational intelligence: A survey. Knowledge-Based Syst 80:14\u201323. https:\/\/doi.org\/10.1016\/j.knosys.2015.01.010","journal-title":"Knowledge-Based Syst"},{"key":"19756_CR14","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/SIBGRAPI-T.2019.00010","volume":"2019","author":"R Ribani","year":"2019","unstructured":"Ribani R, Marengoni M (2019) A Survey of Transfer Learning for Convolutional Neural Networks. Proc - 32nd Conf Graph Patterns Images Tutorials. SIBGRAPI-T 2019:47\u201357. https:\/\/doi.org\/10.1109\/SIBGRAPI-T.2019.00010","journal-title":"SIBGRAPI-T"},{"key":"19756_CR15","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1007\/s10115-013-0665-3","volume":"36","author":"D Cook","year":"2013","unstructured":"Cook D, Feuz KD, Krishnan NC (2013) Transfer Learning for Activity Recognition: A Survey. Knowl Inf Syst 36:537\u2013556","journal-title":"Knowl Inf Syst"},{"key":"19756_CR16","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1007\/s13042-016-0588-x","volume":"9","author":"A Mohammadi","year":"2018","unstructured":"Mohammadi A, Zahiri SH (2018) Inclined planes system optimization algorithm for IIR system identification. Int J Mach Learn Cybern 9:541\u2013558. https:\/\/doi.org\/10.1007\/s13042-016-0588-x","journal-title":"Int J Mach Learn Cybern"},{"key":"19756_CR17","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s10462-016-9500-z","volume":"48","author":"A Mohammadi","year":"2017","unstructured":"Mohammadi A, Zahiri SH (2017) IIR model identification using a modified inclined planes system optimization algorithm. Artif Intell Rev 48:237\u2013259. https:\/\/doi.org\/10.1007\/s10462-016-9500-z","journal-title":"Artif Intell Rev"},{"key":"19756_CR18","doi-asserted-by":"publisher","unstructured":"Mohammadi A, Sheikholeslam F, Mirjalili S (2022) Inclined planes system optimization: theory, literature review, and state-of-the-art versions for IIR system identification. Expert Syst Appl 200:117127. https:\/\/doi.org\/10.1016\/j.eswa.2022.117127","DOI":"10.1016\/j.eswa.2022.117127"},{"key":"19756_CR19","doi-asserted-by":"publisher","unstructured":"Esfahrood SM, Mohammadi A, Zahiri SH (2019) A simplified and efficient version of inclined planes system optimization algorithm. In: 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), pp 504\u2013509. https:\/\/doi.org\/10.1109\/KBEI.2019.8735044","DOI":"10.1109\/KBEI.2019.8735044"},{"issue":"1","key":"19756_CR20","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s11831-022-09800-0","volume":"30","author":"A Mohammadi","year":"2023","unstructured":"Mohammadi A, Sheikholeslam F, Mirjalili S (2023) Nature-inspired metaheuristic search algorithms for optimizing benchmark problems: inclined planes system optimization to state-of-the-art methods. Arch Comput Methods Eng 30(1):331\u2013389. https:\/\/doi.org\/10.1007\/s11831-022-09800-0","journal-title":"Arch Comput Methods Eng"},{"key":"19756_CR21","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/j.neucom.2015.11.059","volume":"177","author":"W Pan","year":"2016","unstructured":"Pan W (2016) A survey of transfer learning for collaborative recommendation with auxiliary data. Neurocomputing 177:447\u2013453. https:\/\/doi.org\/10.1016\/j.neucom.2015.11.059","journal-title":"Neurocomputing"},{"key":"19756_CR22","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1080\/08839514.2019.1603784","volume":"33","author":"SMM Ali","year":"2019","unstructured":"Ali SMM, Augusto JC, Windridge D (2019) A Survey of User-Centred Approaches for Smart Home Transfer Learning and New User Home Automation Adaptation. Appl Artif Intell 33:747\u2013774. https:\/\/doi.org\/10.1080\/08839514.2019.1603784","journal-title":"Appl Artif Intell"},{"key":"19756_CR23","doi-asserted-by":"publisher","first-page":"85401","DOI":"10.1109\/ACCESS.2019.2925059","volume":"7","author":"R Liu","year":"2019","unstructured":"Liu R, Shi Y, Ji C, Jia M (2019) A Survey of Sentiment Analysis Based on Transfer Learning. IEEE Access 7:85401\u201385412. https:\/\/doi.org\/10.1109\/ACCESS.2019.2925059","journal-title":"IEEE Access"},{"key":"19756_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2020.103515","volume":"128","author":"Y Liu","year":"2020","unstructured":"Liu Y, Li Z, Liu H, Kan Z (2020) Skill transfer learning for autonomous robots and human\u2013robot cooperation: A survey. Rob Auton Syst 128:103515. https:\/\/doi.org\/10.1016\/j.robot.2020.103515","journal-title":"Rob Auton Syst"},{"key":"19756_CR25","doi-asserted-by":"publisher","unstructured":"Zhao C (2020) A Survey on Image Style Transfer Approaches Using Deep Learning. J Phys Conf Ser 1453:. https:\/\/doi.org\/10.1088\/1742-6596\/1453\/1\/012129","DOI":"10.1088\/1742-6596\/1453\/1\/012129"},{"key":"19756_CR26","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1109\/TAI.2021.3054609","volume":"1","author":"S Niu","year":"2020","unstructured":"Niu S, Liu Y, Wang J, Song H (2020) A Decade Survey of Transfer Learning (2010\u20132020). IEEE Trans Artif Intell 1:151\u2013166. https:\/\/doi.org\/10.1109\/TAI.2021.3054609","journal-title":"IEEE Trans Artif Intell"},{"key":"19756_CR27","doi-asserted-by":"publisher","first-page":"101830","DOI":"10.1016\/j.sysarc.2020.101830","volume":"108","author":"A Sufian","year":"2020","unstructured":"Sufian A, Ghosh A, Sadiq AS, Smarandache F (2020) A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic: DTL-EC. J Syst Archit 108:101830. https:\/\/doi.org\/10.1016\/j.sysarc.2020.101830","journal-title":"J Syst Archit"},{"key":"19756_CR28","doi-asserted-by":"publisher","first-page":"3178","DOI":"10.1093\/mnras\/staa325","volume":"493","author":"W Wei","year":"2020","unstructured":"Wei W, Huerta EA, Whitmore BC et al (2020) Deep transfer learning for star cluster classification: I. application to the PHANGS-HST survey. Mon Not R Astron Soc 493:3178\u20133193. https:\/\/doi.org\/10.1093\/mnras\/staa325","journal-title":"Mon Not R Astron Soc"},{"key":"19756_CR29","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1109\/SSCI47803.2020.9308468","volume":"2020","author":"W Zhao","year":"2020","unstructured":"Zhao W, Queralta JP (2020) Westerlund T (2020) Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: A Survey. IEEE Symp Ser Comput Intell SSCI 2020:737\u2013744. https:\/\/doi.org\/10.1109\/SSCI47803.2020.9308468","journal-title":"IEEE Symp Ser Comput Intell SSCI"},{"key":"19756_CR30","doi-asserted-by":"publisher","unstructured":"Dhyani B (2021) Transfer Learning in Natural Language Processing: A Survey. Math Stat Eng Appl 70:303\u2013311. https:\/\/doi.org\/10.17762\/msea.v70i1.2312","DOI":"10.17762\/msea.v70i1.2312"},{"key":"19756_CR31","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1007\/978-981-15-5971-6_83","volume":"194","author":"S Panigrahi","year":"2021","unstructured":"Panigrahi S, Nanda A, Swarnkar T (2021) A Survey on Transfer Learning. Smart Innov Syst Technol 194:781\u2013789. https:\/\/doi.org\/10.1007\/978-981-15-5971-6_83","journal-title":"Smart Innov Syst Technol"},{"key":"19756_CR32","doi-asserted-by":"publisher","first-page":"100125","DOI":"10.1016\/j.geits.2023.100125","volume":"2","author":"X Liu","year":"2023","unstructured":"Liu X, Li J, Ma J et al (2023) Deep transfer learning for intelligent vehicle perception: A survey. Green Energy Intell Transp 2:100125. https:\/\/doi.org\/10.1016\/j.geits.2023.100125","journal-title":"Green Energy Intell Transp"},{"key":"19756_CR33","doi-asserted-by":"publisher","unstructured":"Al-Hajj R, Assi A, Neji B, Ghandour R, Al Barakeh Z (2023) Transfer learning for renewable energy systems: a survey. Sustainability 15(11):9131. https:\/\/doi.org\/10.3390\/su15119131","DOI":"10.3390\/su15119131"},{"key":"19756_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-022-10230-4","volume-title":"A survey of transfer learning for machinery diagnostics and prognostics","author":"S Yao","year":"2023","unstructured":"Yao S, Kang Q, Zhou MC et al (2023) A survey of transfer learning for machinery diagnostics and prognostics. Springer, Netherlands"},{"issue":"12","key":"19756_CR35","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.3390\/jpm13121703","volume":"13","author":"L Chato","year":"2023","unstructured":"Chato L, Regentova E (2023) Survey of transfer learning approaches in the machine learning of digital health sensing data. J Pers Med 13(12):1703. https:\/\/doi.org\/10.3390\/jpm13121703","journal-title":"J Pers Med"},{"key":"19756_CR36","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1109\/ACCESS.2023.3343329","volume":"12","author":"R Haque","year":"2024","unstructured":"Haque R, Ali A, Mcclean S et al (2024) Heterogeneous Cross-Project Defect Prediction Using Encoder Networks and Transfer Learning. IEEE Access 12:409\u2013419. https:\/\/doi.org\/10.1109\/ACCESS.2023.3343329","journal-title":"IEEE Access"},{"key":"19756_CR37","doi-asserted-by":"publisher","unstructured":"Xie W, Zhang C, Jia K, et al (2023) Cross-Project Aging-Related Bug Prediction Based on Feature Transfer and Class Imbalance Learning. Proc - 2023 IEEE 34th Int Symp Softw Reliab Eng Work ISSREW 2023 206\u2013213. https:\/\/doi.org\/10.1109\/ISSREW60843.2023.00075","DOI":"10.1109\/ISSREW60843.2023.00075"},{"key":"19756_CR38","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/s11219-021-09553-2","volume":"29","author":"J Wu","year":"2021","unstructured":"Wu J, Wu Y, Niu N, Zhou M (2021) MHCPDP: multi-source heterogeneous cross-project defect prediction via multi-source transfer learning and autoencoder. Softw Qual J 29:405\u2013430. https:\/\/doi.org\/10.1007\/s11219-021-09553-2","journal-title":"Softw Qual J"},{"key":"19756_CR39","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.infsof.2018.11.005","volume":"107","author":"C Liu","year":"2019","unstructured":"Liu C, Yang D, Xia X et al (2019) A two-phase transfer learning model for cross-project defect prediction. Inf Softw Technol 107:125\u2013136. https:\/\/doi.org\/10.1016\/j.infsof.2018.11.005","journal-title":"Inf Softw Technol"},{"key":"19756_CR40","doi-asserted-by":"publisher","unstructured":"Li K, Xiang Z, Chen T, Wang S, Tan KC (2020) Understanding the automated parameter optimization on transfer learning for cross-project defect prediction: an empirical study. In: Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering (ICSE '20). Association for Computing Machinery, New York, NY, pp 566\u2013577. https:\/\/doi.org\/10.1145\/3377811.3380360","DOI":"10.1145\/3377811.3380360"},{"key":"19756_CR41","doi-asserted-by":"publisher","unstructured":"Chen Y, Dai H (2021) Improving cross-project defect prediction with weighted software modules via transfer learning. J Phys Conf Ser 2025:. https:\/\/doi.org\/10.1088\/1742-6596\/2025\/1\/012100","DOI":"10.1088\/1742-6596\/2025\/1\/012100"},{"key":"19756_CR42","doi-asserted-by":"publisher","unstructured":"Zeng F, Lin W, Xing Y, et al (2022) A Cross-project Defect Prediction Model Using Feature Transfer and Ensemble Learning. Teh Vjesn 29:1089\u20131099. https:\/\/doi.org\/10.17559\/TV-20220421110027","DOI":"10.17559\/TV-20220421110027"},{"key":"19756_CR43","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1049\/cje.2021.00.119","volume":"31","author":"T Lei","year":"2022","unstructured":"Lei T, Xue J, Wang Y et al (2022) WCM-WTrA: A Cross-Project Defect Prediction Method Based on Feature Selection and Distance-Weight Transfer Learning. Chinese J Electron 31:354\u2013366. https:\/\/doi.org\/10.1049\/cje.2021.00.119","journal-title":"Chinese J Electron"},{"key":"19756_CR44","doi-asserted-by":"publisher","unstructured":"Tang S, Huang S, Zheng C, et al (2022) A novel cross-project software defect prediction algorithm based on transfer learning. Tsinghua Sci Technol 27:41\u201357. https:\/\/doi.org\/10.26599\/TST.2020.9010040","DOI":"10.26599\/TST.2020.9010040"},{"key":"19756_CR45","doi-asserted-by":"publisher","first-page":"101286","DOI":"10.1016\/j.softx.2022.101286","volume":"21","author":"J Zou","year":"2023","unstructured":"Zou J, Li Z, Liu X, Tong H (2023) MSCPDPLab: A MATLAB toolbox for transfer learning based multi-source cross-project defect prediction. SoftwareX 21:101286. https:\/\/doi.org\/10.1016\/j.softx.2022.101286","journal-title":"SoftwareX"},{"key":"19756_CR46","doi-asserted-by":"publisher","first-page":"106985","DOI":"10.1016\/j.infsof.2022.106985","volume":"150","author":"J Bai","year":"2022","unstructured":"Bai J, Jia J, Capretz LF (2022) A three-stage transfer learning framework for multi-source cross-project software defect prediction. Inf Softw Technol 150:106985. https:\/\/doi.org\/10.1016\/j.infsof.2022.106985","journal-title":"Inf Softw Technol"},{"key":"19756_CR47","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s11219-019-09467-0","volume":"28","author":"X Du","year":"2020","unstructured":"Du X, Zhou Z, Yin B, Xiao G (2020) Cross-project bug type prediction based on transfer learning. Softw Qual J 28:39\u201357. https:\/\/doi.org\/10.1007\/s11219-019-09467-0","journal-title":"Softw Qual J"},{"key":"19756_CR48","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1007\/s11390-019-1959-z","volume":"34","author":"Z Xu","year":"2019","unstructured":"Xu Z, Pang S, Zhang T et al (2019) Cross Project Defect Prediction via Balanced Distribution Adaptation Based Transfer Learning. J Comput Sci Technol 34:1039\u20131062. https:\/\/doi.org\/10.1007\/s11390-019-1959-z","journal-title":"J Comput Sci Technol"},{"key":"19756_CR49","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1109\/ICST.2013.38","volume":"2013","author":"G Canfora","year":"2013","unstructured":"Canfora G, De Lucia A, Di Penta M et al (2013) Multi-objective cross-project defect prediction. Proc - IEEE 6th Int Conf Softw Testing. Verif Validation, ICST 2013:252\u2013261. https:\/\/doi.org\/10.1109\/ICST.2013.38","journal-title":"Verif Validation, ICST"},{"key":"19756_CR50","doi-asserted-by":"publisher","unstructured":"Hosseini S, Turhan B, M\u00e4ntyl\u00e4 M (2016) Search based training data selection for cross project defect prediction. In: Proceedings of the 12th international conference on predictive models and data analytics in software engineering, pp 1\u201310. https:\/\/doi.org\/10.1145\/2972958.2972964","DOI":"10.1145\/2972958.2972964"},{"issue":"8","key":"19756_CR51","doi-asserted-by":"publisher","first-page":"216","DOI":"10.3390\/fi13080216","volume":"13","author":"Y Zhao","year":"2021","unstructured":"Zhao Y, Zhu Y, Yu Q, Chen X (2021) Cross-project defect prediction method based on manifold feature transformation. Future Internet 13(8):216. https:\/\/doi.org\/10.3390\/fi13080216","journal-title":"Future Internet"},{"key":"19756_CR52","first-page":"531","volume":"12","author":"W Rhmann","year":"2020","unstructured":"Rhmann W (2020) Cross project defect prediction using hybrid search based algorithms. Int J Inf Technol 12:531\u2013538","journal-title":"Int J Inf Technol"},{"key":"19756_CR53","doi-asserted-by":"publisher","first-page":"114637","DOI":"10.1016\/j.eswa.2021.114637","volume":"171","author":"C Jin","year":"2021","unstructured":"Jin C (2021) Cross-project software defect prediction based on domain adaptation learning and optimization. Expert Syst Appl 171:114637. https:\/\/doi.org\/10.1016\/j.eswa.2021.114637","journal-title":"Expert Syst Appl"},{"key":"19756_CR54","doi-asserted-by":"publisher","unstructured":"Deepalakshmi J, Chandran M (2022) An optimized clustering model for heterogeneous cross-project defect prediction using Quantum Crow search. In: 1st Int Conf Softw Eng Inf Technol (ICoSEIT), pp 30\u201335. https:\/\/doi.org\/10.1109\/ICoSEIT55604.2022.10030011","DOI":"10.1109\/ICoSEIT55604.2022.10030011"},{"key":"19756_CR55","doi-asserted-by":"publisher","unstructured":"Xing Y, Lin W, Lin X, Yang B, Tan Z (2022) Cross\u2010project defect prediction based on two\u2010phase feature importance amplification. Comput Intell Neurosci 1:2320447. https:\/\/doi.org\/10.1155\/2022\/2320447","DOI":"10.1155\/2022\/2320447"},{"key":"19756_CR56","unstructured":"Aljaidi M, Gul S, Faiz R, Samara G, Alsarhan A, al-Qerem A (2023) Impact evaluation of significant feature set in cross project for defect prediction through hybrid feature selection in multiclass. bioRxiv 2023-07"},{"key":"19756_CR57","doi-asserted-by":"publisher","unstructured":"Hu Z, Zhu Y (2023) Cross-project defect prediction method based on genetic algorithm feature selection. Eng Reports 1\u201315. https:\/\/doi.org\/10.1002\/eng2.12670","DOI":"10.1002\/eng2.12670"},{"key":"19756_CR58","doi-asserted-by":"publisher","unstructured":"Faiz R bin, Shaheen S, Sharaf M, Rauf HT (2023) Optimal Feature Selection through Search-Based Optimizer in Cross Project. Electron 12:. https:\/\/doi.org\/10.3390\/electronics12030514","DOI":"10.3390\/electronics12030514"},{"key":"19756_CR59","first-page":"619","volume":"12","author":"DP Gottumukkala","year":"2024","unstructured":"Gottumukkala DP, Ushasree D, Suneetha TV (2024) Software Defect Prediction Through Effective Weighted Optimization Model for Assured Software Quality. Int J Intell Syst Appl Eng 12:619\u2013633","journal-title":"Int J Intell Syst Appl Eng"},{"key":"19756_CR60","doi-asserted-by":"crossref","unstructured":"Hu Z, Zhu Y (2023) Cross\u2010project defect prediction method based on genetic algorithm feature selection. Engineering Reports 5(12): e12670. https:\/\/doi.org\/10.1002\/eng2.12670","DOI":"10.1002\/eng2.12670"},{"key":"19756_CR61","doi-asserted-by":"crossref","unstructured":"Faiz RB, Shaheen S, Sharaf M, Rauf HT (2023) Optimal feature selection through search-based optimizer in cross project. Electronics 12(3): 514. https:\/\/doi.org\/10.3390\/electronics12030514","DOI":"10.3390\/electronics12030514"},{"key":"19756_CR62","doi-asserted-by":"publisher","unstructured":"Kitchenham BA (2012) Systematic review in software engineering: where we are and where we should be going. Proc 2nd Int Work Evidential Assess Softw Technol (EAST \u201912) 1\u20132. https:\/\/doi.org\/10.1145\/2372233.2372235","DOI":"10.1145\/2372233.2372235"},{"key":"19756_CR63","doi-asserted-by":"crossref","unstructured":"Malhotra R (2016) Empirical research in software engineering: concepts, analysis, and applications. CRC press.","DOI":"10.1201\/b19292"},{"key":"19756_CR64","unstructured":"Pratt LY (1992) Discriminability-based transfer between neural networks. Advances in Neural Information Processing Systems 5:204\u2013211"},{"key":"19756_CR65","doi-asserted-by":"publisher","unstructured":"Feuz KD, Cook DJ (2015) Transfer learning across feature-rich heterogeneous feature spaces via feature-space remapping (FSR). ACM Trans Intell Syst Technol 6:. https:\/\/doi.org\/10.1145\/2629528","DOI":"10.1145\/2629528"},{"key":"19756_CR66","first-page":"299","volume":"18","author":"CB Do","year":"2005","unstructured":"Do CB, Ng AY (2005) Transfer learning for text classification. Adv Neural Inf Process Syst 18:299\u2013306","journal-title":"Adv Neural Inf Process Syst"},{"key":"19756_CR67","doi-asserted-by":"publisher","first-page":"2389","DOI":"10.1109\/ACCESS.2017.2782884","volume":"6","author":"X Liu","year":"2017","unstructured":"Liu X, Liu Z, Wang G et al (2017) Ensemble Transfer Learning Algorithm. IEEE. Access 6:2389\u20132396. https:\/\/doi.org\/10.1109\/ACCESS.2017.2782884","journal-title":"Access"},{"key":"19756_CR68","doi-asserted-by":"publisher","unstructured":"Rana R, Ng AY, Koller D (2006) Constructing informative priors using transfer learning. In: Proceedings of the 23rd international conference on Machine learning, pp 713\u2013720. https:\/\/doi.org\/10.1145\/1143844.1143934","DOI":"10.1145\/1143844.1143934"},{"key":"19756_CR69","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.jss.2017.06.070","volume":"132","author":"Q Yu","year":"2017","unstructured":"Yu Q, Jiang S, Zhang Y (2017) A feature matching and transfer approach for cross-company defect prediction. J Syst Softw 132:366\u2013378. https:\/\/doi.org\/10.1016\/j.jss.2017.06.070","journal-title":"J Syst Softw"},{"key":"19756_CR70","first-page":"608","volume":"7","author":"L Mihalkova","year":"2007","unstructured":"Mihalkova L, Huynh T, Mooney RJRJ (2007) Mapping and revising Markov logic networks for transfer learning. Aaai 7:608\u2013614","journal-title":"Aaai"},{"key":"19756_CR71","doi-asserted-by":"publisher","unstructured":"Weiss K, Khoshgoftaar T (2018) Evaluation of transfer learning algorithms using different base learners. Proc - Int Conf Tools with Artif Intell ICTAI 2017-Novem:187\u2013196. https:\/\/doi.org\/10.1109\/ICTAI.2017.00039","DOI":"10.1109\/ICTAI.2017.00039"},{"key":"19756_CR72","doi-asserted-by":"publisher","unstructured":"Pan SJ, Kwok JT, Yang Q (2008) Transfer learning via dimensionality reduction. Proceedeings 23th AAAI Conf Artif Intell 677\u2013682. https:\/\/doi.org\/10.1109\/TKDE.2009.191","DOI":"10.1109\/TKDE.2009.191"},{"key":"19756_CR73","doi-asserted-by":"publisher","unstructured":"Pereira FLF, Dos Santos Lima FD, De Moura Leite LG, et al (2017) Transfer learning for Bayesian networks with application on hard disk drives failure prediction. Proc - 2017 Brazilian Conf Intell Syst BRACIS 2017 2018-Janua:228\u2013233. https:\/\/doi.org\/10.1109\/BRACIS.2017.64","DOI":"10.1109\/BRACIS.2017.64"},{"key":"19756_CR74","doi-asserted-by":"publisher","unstructured":"Dai W, Jin O, Xue GR, et al (2009) Eigentransfer: a unified framework for transfer learning. Proc 26th Annu Int Conf Mach Learn 193\u2013200. https:\/\/doi.org\/10.1145\/1553374.1553399","DOI":"10.1145\/1553374.1553399"},{"key":"19756_CR75","doi-asserted-by":"publisher","unstructured":"Gargees R, Keller J, Popescu M (2017) Early illness recognition in older adults using transfer learning. Proc - 2017 IEEE Int Conf Bioinforma Biomed BIBM 2017 2017-Janua:1012\u20131016. https:\/\/doi.org\/10.1109\/BIBM.2017.8217795","DOI":"10.1109\/BIBM.2017.8217795"},{"key":"19756_CR76","doi-asserted-by":"publisher","unstructured":"Li B, Yang Q, Xue X (2009) Transfer learning for collaborative filtering via a rating-matrix generative model. 1\u20138. https:\/\/doi.org\/10.1145\/1553374.1553454","DOI":"10.1145\/1553374.1553454"},{"key":"19756_CR77","doi-asserted-by":"publisher","unstructured":"Yan S, Shen B, Mo W, Li N (2018) Transfer Learning for Cross-Platform Software Crowdsourcing Recommendation. Proc - Asia-Pacific Softw Eng Conf APSEC 2017-Decem:269\u2013278. https:\/\/doi.org\/10.1109\/APSEC.2017.33","DOI":"10.1109\/APSEC.2017.33"},{"key":"19756_CR78","doi-asserted-by":"publisher","unstructured":"Wan J, Wang X, Yin Y, Zhou R (2015) Transfer Learning in Collaborative Filtering for Sparsity Reduction Via Feature Tags Learning Model. 56\u201360. https:\/\/doi.org\/10.14257\/astl.2015.81.12","DOI":"10.14257\/astl.2015.81.12"},{"key":"19756_CR79","doi-asserted-by":"publisher","unstructured":"Chen Y, Ding X (2018) Research on cross - Project software defect prediction based on transfer learning. AIP Conf Proc 1955:. https:\/\/doi.org\/10.1063\/1.5033747","DOI":"10.1063\/1.5033747"},{"key":"19756_CR80","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 54:248\u2013256. https:\/\/doi.org\/10.1016\/j.infsof.2011.09.007","journal-title":"Inf Softw Technol"},{"key":"19756_CR81","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1109\/TSE.2018.2821670","volume":"45","author":"R Krishna","year":"2019","unstructured":"Krishna R, Menzies T (2019) Bellwethers: A Baseline Method for Transfer Learning. IEEE Trans Softw Eng 45:1081\u20131105. https:\/\/doi.org\/10.1109\/TSE.2018.2821670","journal-title":"IEEE Trans Softw Eng"},{"key":"19756_CR82","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1609\/aaai.v26i1.8290","volume":"2","author":"M Long","year":"2012","unstructured":"Long M, Wang J, Ding G et al (2012) Transfer learning with graph co-regularization. Proc Natl Conf Artif Intell 2:1033\u20131039. https:\/\/doi.org\/10.1609\/aaai.v26i1.8290","journal-title":"Proc Natl Conf Artif Intell"},{"key":"19756_CR83","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 et al (2018) Heterogeneous Defect Prediction. IEEE Trans Softw Eng 44:874\u2013896. https:\/\/doi.org\/10.1109\/TSE.2017.2720603","journal-title":"IEEE Trans Softw Eng"},{"key":"19756_CR84","doi-asserted-by":"publisher","unstructured":"Nam J, Pan SJ, Kim S (2013) Transfer defect learning. Proc - Int Conf Softw Eng 382\u2013391. https:\/\/doi.org\/10.1109\/ICSE.2013.6606584","DOI":"10.1109\/ICSE.2013.6606584"},{"key":"19756_CR85","first-page":"160","volume":"2018","author":"AA Deshmukh","year":"2018","unstructured":"Deshmukh AA (2018) SEMI-SUPERVISED TRANSFER LEARNING USING MARGINAL PREDICTORS University of Michigan Electrical Engineering and Computer Science Emil Laftchiev Mitsubishi Electric Research Labs Data Analytics Cambridge, MA 02139. IEEE Data Sci Work 2018:160\u2013164","journal-title":"IEEE Data Sci Work"},{"key":"19756_CR86","doi-asserted-by":"publisher","first-page":"2213","DOI":"10.1609\/aaai.v28i1.8961","volume":"3","author":"JT Zhou","year":"2014","unstructured":"Zhou JT, Pan SJ, Tsang IW, Yan Y (2014) Hybrid heterogeneous transfer learning through deep learning. Proc Natl Conf Artif Intell 3:2213\u20132219. https:\/\/doi.org\/10.1609\/aaai.v28i1.8961","journal-title":"Proc Natl Conf Artif Intell"},{"key":"19756_CR87","first-page":"5085","volume":"80","author":"Y Wei","year":"2018","unstructured":"Wei Y, Zhang Y, Huang J, Yang Q (2018) Transfer Learning via Learning to Transfer. Icml 80:5085\u20135094","journal-title":"Icml"},{"key":"19756_CR88","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1007\/s10664-014-9300-5","volume":"20","author":"E Kocaguneli","year":"2015","unstructured":"Kocaguneli E, Menzies T, Mendes E (2015) Transfer learning in effort estimation. Empir Softw Eng 20:813\u2013843. https:\/\/doi.org\/10.1007\/s10664-014-9300-5","journal-title":"Empir Softw Eng"},{"key":"19756_CR89","doi-asserted-by":"publisher","unstructured":"Cui Y, Song Y, Sun C, et al (2018) Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 4109\u20134118. https:\/\/doi.org\/10.1109\/CVPR.2018.00432","DOI":"10.1109\/CVPR.2018.00432"},{"key":"19756_CR90","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1108\/IJPCC-03-2014-0020","volume":"10","author":"KD Feuz","year":"2014","unstructured":"Feuz KD, Cook DJ (2014) Heterogeneous transfer learning for activity recognition using heuristic search techniques. Int J Pervasive Comput Commun 10:393\u2013418. https:\/\/doi.org\/10.1108\/IJPCC-03-2014-0020","journal-title":"Int J Pervasive Comput Commun"},{"key":"19756_CR91","doi-asserted-by":"publisher","first-page":"8901","DOI":"10.1109\/ACCESS.2018.2890733","volume":"7","author":"J Chen","year":"2019","unstructured":"Chen J, Yang Y, Hu K et al (2019) Multiview transfer learning for software defect prediction. IEEE Access 7:8901\u20138916. https:\/\/doi.org\/10.1109\/ACCESS.2018.2890733","journal-title":"IEEE Access"},{"key":"19756_CR92","doi-asserted-by":"publisher","unstructured":"Qing H, Biwen L, Beijun S, Xia Y (2015) Cross-project software defect prediction using feature-based transfer learning. In: Proceedings of the 7th Asia-Pacific Symposium on Internetware, pp 74\u201382. https:\/\/doi.org\/10.1145\/2875913.2875944","DOI":"10.1145\/2875913.2875944"},{"key":"19756_CR93","doi-asserted-by":"publisher","unstructured":"Tong H, Liu B, Wang S, Li Q (2019) Transfer-learning oriented class imbalance learning for cross-project defect prediction. https:\/\/doi.org\/10.48550\/arXiv.1901.08429","DOI":"10.48550\/arXiv.1901.08429"},{"key":"19756_CR94","doi-asserted-by":"publisher","unstructured":"Jing X, Wu F, Dong X, et al (2015) Heterogeneous cross-company defect prediction by unified metric representation and CCA-based transfer learning. 2015 10th Jt Meet Eur Softw Eng Conf ACM SIGSOFT Symp Found Softw Eng ESEC\/FSE 2015 - Proc 496\u2013507. https:\/\/doi.org\/10.1145\/2786805.2786813","DOI":"10.1145\/2786805.2786813"},{"key":"19756_CR95","doi-asserted-by":"publisher","unstructured":"Cao Q, Sun Q, Cao Q, Tan H (2015) Software defect prediction via transfer learning based neural network. Proc 2015 1st Int Conf Reliab Syst Eng ICRSE 2015. https:\/\/doi.org\/10.1109\/ICRSE.2015.7366475","DOI":"10.1109\/ICRSE.2015.7366475"},{"key":"19756_CR96","doi-asserted-by":"publisher","unstructured":"Krishna R, Menzies T, Fu W (2016) Too much automation? the bellwether effect and its implications for transfer learning. ASE 2016 - Proc 31st IEEE\/ACM Int Conf Autom Softw Eng 122\u2013131. https:\/\/doi.org\/10.1145\/2970276.2970339","DOI":"10.1145\/2970276.2970339"},{"key":"19756_CR97","doi-asserted-by":"publisher","unstructured":"Weiss KR, Khoshgoftaar TM (2017) An investigation of transfer learning and traditional machine learning algorithms. Proc - 2016 IEEE 28th Int Conf Tools with Artif Intell ICTAI 2016 283\u2013290. https:\/\/doi.org\/10.1109\/ICTAI.2016.48","DOI":"10.1109\/ICTAI.2016.48"},{"key":"19756_CR98","doi-asserted-by":"publisher","unstructured":"Su KM, Robbins KA, Hairston WD (2017) Adaptive thresholding and reweighting to improve domain transfer learning for unbalanced data with applications to EEG imbalance. Proc - 2016 15th IEEE Int Conf Mach Learn Appl ICMLA 2016 320\u2013325. https:\/\/doi.org\/10.1109\/ICMLA.2016.34","DOI":"10.1109\/ICMLA.2016.34"},{"key":"19756_CR99","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1109\/TSE.2016.2597849","volume":"43","author":"XY Jing","year":"2017","unstructured":"Jing XY, Wu F, Dong X, Xu B (2017) An Improved SDA Based Defect Prediction Framework for Both Within-Project and Cross-Project Class-Imbalance Problems. IEEE Trans Softw Eng 43:321\u2013339. https:\/\/doi.org\/10.1109\/TSE.2016.2597849","journal-title":"IEEE Trans Softw Eng"},{"key":"19756_CR100","doi-asserted-by":"publisher","unstructured":"Wu F, Jing XY, Dong X, et al (2017) Cross-project and within-project semi-supervised software defect prediction problems study using a unified solution. In: Proceedings - 2017 IEEE\/ACM 39th International Conference on Software Engineering Companion, ICSE-C 2017. Inst Electr Electron Eng Inc 195\u2013197. https:\/\/doi.org\/10.1109\/ICSE-C.2017.72","DOI":"10.1109\/ICSE-C.2017.72"},{"key":"19756_CR101","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1109\/TPAMI.2011.114","volume":"34","author":"L Duan","year":"2012","unstructured":"Duan L, Tsang IW, Xu D (2012) Domain transfer multiple kernel learning. IEEE Trans Pattern Anal Mach Intell 34:465\u2013479. https:\/\/doi.org\/10.1109\/TPAMI.2011.114","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"19756_CR102","unstructured":"Wei Y, Zhang Y, Huang J, Yang Q (2018) Transfer learning via learning to transfer. 35th Int Conf Mach Learn ICML 11:8059. https:\/\/doi.org\/1783.1\/92190"},{"key":"19756_CR103","doi-asserted-by":"publisher","unstructured":"Weiss KR, Khoshgoftaar TM (2017) Detection of Phishing Webpages Using Heterogeneous Transfer Learning. Proc - 2017 IEEE 3rd Int Conf Collab Internet Comput CIC 2017 2017-Janua:190\u2013197. https:\/\/doi.org\/10.1109\/CIC.2017.00034","DOI":"10.1109\/CIC.2017.00034"},{"key":"19756_CR104","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1109\/TKDE.2017.2669193","volume":"29","author":"Y Xu","year":"2017","unstructured":"Xu Y, Pan SJ, Xiong H et al (2017) A Unified Framework for Metric Transfer Learning. IEEE Trans Knowl Data Eng 29:1158\u20131171. https:\/\/doi.org\/10.1109\/TKDE.2017.2669193","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19756-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19756-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19756-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T13:21:43Z","timestamp":1732022503000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19756-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,27]]},"references-count":104,"journal-issue":{"issue":"39","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["19756"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19756-x","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,27]]},"assertion":[{"value":"13 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval and consent to participate"}},{"value":"The authors declare that they have no conflicts of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"The authors provide consent for publication.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}]}}