{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T07:42:10Z","timestamp":1776498130808,"version":"3.51.2"},"reference-count":88,"publisher":"Springer Science and Business Media LLC","issue":"27","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s00521-024-09930-5","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T03:09:10Z","timestamp":1717384150000},"page":"16911-16940","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction"],"prefix":"10.1007","volume":"36","author":[{"given":"Manar","family":"Abu Talib","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Bou Nassif","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Azzeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaser","family":"Alesh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaman","family":"Afadar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,3]]},"reference":[{"issue":"10","key":"9930_CR1","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 Comput Surv 55(10):1\u201335. https:\/\/doi.org\/10.1145\/3567550","journal-title":"ACM Comput Surv"},{"key":"9930_CR2","doi-asserted-by":"publisher","first-page":"137613","DOI":"10.1109\/ACCESS.2021.3117989","volume":"9","author":"I Atoum","year":"2021","unstructured":"Atoum I et al (2021) Challenges of software requirements quality assurance and validation: a systematic literature review. IEEE Access 9:137613\u2013137634. https:\/\/doi.org\/10.1109\/ACCESS.2021.3117989","journal-title":"IEEE Access"},{"key":"9930_CR3","doi-asserted-by":"publisher","DOI":"10.25195\/ijci.v46i1.249","author":"AM Altaie","year":"2020","unstructured":"Altaie AM, Alsarraj RG, Al-Bayati AH (2020) Verification and validation of a software: a review of the literature. Iraqi J Comput Inform. https:\/\/doi.org\/10.25195\/ijci.v46i1.249","journal-title":"Iraqi J Comput Inform"},{"key":"9930_CR4","doi-asserted-by":"publisher","first-page":"140896","DOI":"10.1109\/ACCESS.2021.3119746","volume":"9","author":"S Shafiq","year":"2021","unstructured":"Shafiq S, Mashkoor A, Mayr-Dorn C, Egyed A (2021) A literature review of using machine learning in software development life cycle stages. IEEE Access 9:140896\u2013140920. https:\/\/doi.org\/10.1109\/ACCESS.2021.3119746","journal-title":"IEEE Access"},{"key":"9930_CR5","unstructured":"Kalaivani N, Beena DR. Overview of software defect prediction using machine learning algorithms"},{"key":"9930_CR6","doi-asserted-by":"publisher","unstructured":"Deepa N, Prabadevi B, Krithika LB, Deepa B (2020) An analysis on version control systems. In: 2020 international conference on emerging trends in information technology and engineering (ic-ETITE), pp 1\u20139. https:\/\/doi.org\/10.1109\/ic-ETITE47903.2020.39","DOI":"10.1109\/ic-ETITE47903.2020.39"},{"issue":"3","key":"9930_CR7","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1145\/383876.383878","volume":"10","author":"DE Perry","year":"2001","unstructured":"Perry DE, Siy HP, Votta LG (2001) Parallel changes in large-scale software development: an observational case study. ACM Trans Softw Eng Methodol 10(3):308\u2013337. https:\/\/doi.org\/10.1145\/383876.383878","journal-title":"ACM Trans Softw Eng Methodol"},{"key":"9930_CR8","doi-asserted-by":"publisher","first-page":"111245","DOI":"10.1016\/j.jss.2022.111245","volume":"188","author":"W Zheng","year":"2022","unstructured":"Zheng W, Shen T, Chen X, Deng P (2022) Interpretability application of the Just-in-Time software defect prediction model. J Syst Softw 188:111245. https:\/\/doi.org\/10.1016\/j.jss.2022.111245","journal-title":"J Syst Softw"},{"key":"9930_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.infsof.2017.08.004","volume":"93","author":"X Chen","year":"2018","unstructured":"Chen X, Zhao Y, Wang Q, Yuan Z (2018) MULTI: MULTI-objective effort-aware just-in-time software defect prediction. Inf Softw Technol 93:1\u201313. https:\/\/doi.org\/10.1016\/j.infsof.2017.08.004","journal-title":"Inf Softw Technol"},{"issue":"4","key":"9930_CR10","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1109\/TSE.2020.3021380","volume":"48","author":"G Rodriguez-Perez","year":"2022","unstructured":"Rodriguez-Perez G, Nagappan M, Robles G (2022) Watch out for extrinsic bugs! A case study of their impact in just-in-time bug prediction models on the OpenStack project. IIEEE Trans Software Eng 48(4):1400\u20131416. https:\/\/doi.org\/10.1109\/TSE.2020.3021380","journal-title":"IIEEE Trans Software Eng"},{"issue":"4","key":"9930_CR11","doi-asserted-by":"publisher","first-page":"102:1","DOI":"10.1145\/3582572","volume":"32","author":"Z Guo","year":"2023","unstructured":"Guo Z et al (2023) Code-line-level bugginess identification: How far have we come, and how far have we yet to go? ACM Trans Softw Eng Methodol 32(4):102:1-102:55. https:\/\/doi.org\/10.1145\/3582572","journal-title":"ACM Trans Softw Eng Methodol"},{"issue":"5","key":"9930_CR12","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1109\/TSE.2017.2693980","volume":"44","author":"S McIntosh","year":"2018","unstructured":"McIntosh S, Kamei Y (2018) Are fix-inducing changes a moving target? A longitudinal case study of just-in-time defect prediction. IEEE Trans Softw Eng 44(5):412\u2013428. https:\/\/doi.org\/10.1109\/TSE.2017.2693980","journal-title":"IEEE Trans Softw Eng"},{"issue":"2","key":"9930_CR13","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1109\/TR.2021.3060937","volume":"70","author":"K Zhao","year":"2021","unstructured":"Zhao K, Xu Z, Zhang TZ, Tang Y, Yan M (2021) Simplified deep forest model based just-in-time defect prediction for Android mobile apps. IEEE Trans Reliab 70(2):848\u2013859. https:\/\/doi.org\/10.1109\/TR.2021.3060937","journal-title":"IEEE Trans Reliab"},{"key":"9930_CR14","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.jss.2018.12.001","volume":"150","author":"L Pascarella","year":"2019","unstructured":"Pascarella L, Palomba F, Bacchelli A (2019) Fine-grained just-in-time defect prediction. J Syst Softw 150:22\u201336. https:\/\/doi.org\/10.1016\/j.jss.2018.12.001","journal-title":"J Syst Softw"},{"issue":"2","key":"9930_CR15","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 Tech J 5(2):169\u2013180. https:\/\/doi.org\/10.1002\/bltj.2229","journal-title":"Bell Labs Tech J"},{"key":"9930_CR16","unstructured":"Classifying software changes: clean or buggy?|IEEE J Mag|IEEE Xplore. https:\/\/ieeexplore.ieee.org\/abstract\/document\/4408585?casa_token=6gNOv22PUhcAAAAA:x2acRLhWC2b4d8UhHxJwuqUmHG7BX0N92JXvtld1p-iSEsRx5D2VZitNTqHNqiM9UEukbI_oBJfL. Accessed 08 Aug 2023"},{"key":"9930_CR17","unstructured":"Keshavarz H (2022) JITGNN: a deep graph neural network for just-in-time bug prediction. Master thesis, University of Waterloo. https:\/\/uwspace.uwaterloo.ca\/handle\/10012\/18248. Accessed 03 Jun 2023"},{"key":"9930_CR18","doi-asserted-by":"publisher","unstructured":"Hoang T, Khanh Dam H, 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. https:\/\/doi.org\/10.1109\/MSR.2019.00016","DOI":"10.1109\/MSR.2019.00016"},{"key":"9930_CR19","doi-asserted-by":"publisher","unstructured":"Hoang T, Kang HJ, Lawall J, Lo D (2020) CC2Vec: distributed representations of code changes. In: Proceedings of the ACM\/IEEE 42nd international conference on software engineering, pp 518\u2013529. https:\/\/doi.org\/10.1145\/3377811.3380361","DOI":"10.1145\/3377811.3380361"},{"key":"9930_CR20","doi-asserted-by":"publisher","unstructured":"Pornprasit C, Tantithamthavorn CK (20021) 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. https:\/\/doi.org\/10.1109\/MSR52588.2021.00049","DOI":"10.1109\/MSR52588.2021.00049"},{"key":"9930_CR21","doi-asserted-by":"publisher","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, in ISSTA 2021. Association for Computing Machinery, New York, NY, USA, pp 427\u2013438. https:\/\/doi.org\/10.1145\/3460319.3464819","DOI":"10.1145\/3460319.3464819"},{"key":"9930_CR22","doi-asserted-by":"publisher","unstructured":"Keshavarz H, Nagappan M (2022) ApacheJIT: a large dataset for just-in-time defect prediction. In: 2022 IEEE\/ACM 19th international conference on mining software repositories (MSR), pp 191\u2013195. https:\/\/doi.org\/10.1145\/3524842.3527996","DOI":"10.1145\/3524842.3527996"},{"key":"9930_CR23","doi-asserted-by":"publisher","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, pp 99\u2013108. https:\/\/doi.org\/10.1109\/ICSE.2015.139","DOI":"10.1109\/ICSE.2015.139"},{"key":"9930_CR24","unstructured":"Vaswani A et al (2017) Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems, in NIPS\u201917. Curran Associates Inc., Red Hook, NY, USA, pp 6000\u20136010"},{"key":"9930_CR25","doi-asserted-by":"publisher","unstructured":"Wang Y, Wang W, Joty S, Hoi SCH (2021) CodeT5: identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. In: Proceedings of the 2021 conference on empirical methods in natural language processing, online and Punta Cana, Dominican Republic: Association for Computational Linguistics, pp 8696\u20138708. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.685","DOI":"10.18653\/v1\/2021.emnlp-main.685"},{"key":"9930_CR26","doi-asserted-by":"crossref","unstructured":"Wang Y, Le H, Gotmare AD, Bui NDQ, Li J, Hoi SCH (2023) CodeT5+: open code large language models for code understanding and generation. arXiv. http:\/\/arxiv.org\/abs\/2305.07922. Accessed 12 Jun 2023","DOI":"10.18653\/v1\/2023.emnlp-main.68"},{"key":"9930_CR27","doi-asserted-by":"crossref","unstructured":"Lin B, Wang S, Liu Z, Liu Y, Xia X, Mao X (2023) CCT5: a code-change-oriented pre-trained model","DOI":"10.1145\/3611643.3616339"},{"key":"9930_CR28","doi-asserted-by":"publisher","unstructured":"Liu Z, Tang Z, Xia X, Yang X (2023) CCRep: learning code change representations via pre-trained code model and query back. In: 2023 IEEE\/ACM 45th international conference on software engineering (ICSE), pp 17\u201329. https:\/\/doi.org\/10.1109\/ICSE48619.2023.00014","DOI":"10.1109\/ICSE48619.2023.00014"},{"key":"9930_CR29","doi-asserted-by":"publisher","first-page":"111283","DOI":"10.1016\/j.jss.2022.111283","volume":"188","author":"F Lomio","year":"2022","unstructured":"Lomio F, Iannone E, De Lucia A, Palomba F, Lenarduzzi V (2022) Just-in-time software vulnerability detection: Are we there yet? J Syst Softw 188:111283. https:\/\/doi.org\/10.1016\/j.jss.2022.111283","journal-title":"J Syst Softw"},{"issue":"6","key":"9930_CR30","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1109\/TSE.2012.70","volume":"39","author":"Y Kamei","year":"2013","unstructured":"Kamei Y et al (2013) A large-scale empirical study of just-in-time quality assurance. IEEE Trans Softw Eng 39(6):757\u2013773. https:\/\/doi.org\/10.1109\/TSE.2012.70","journal-title":"IEEE Trans Softw Eng"},{"key":"9930_CR31","doi-asserted-by":"publisher","unstructured":"Catolino G, Di Nucci D, Ferrucci F (2019) Cross-project just-in-time bug prediction for mobile apps: an empirical assessment. In: 2019 IEEE\/ACM 6th international conference on mobile software engineering and systems (MOBILESoft), pp 99\u2013110. https:\/\/doi.org\/10.1109\/MOBILESoft.2019.00023","DOI":"10.1109\/MOBILESoft.2019.00023"},{"key":"9930_CR32","unstructured":"Zhou Z-H, Feng J (2020) Deep forest. arXiv. http:\/\/arxiv.org\/abs\/1702.08835. Accessed 13 Aug 2023"},{"key":"9930_CR33","unstructured":"Seo PH, Lin Z, Cohen S, Shen X, Han B (2016) Progressive attention networks for visual attribute prediction. arXiv. http:\/\/arxiv.org\/abs\/1606.02393. Accessed 13 Aug 2023"},{"key":"9930_CR34","doi-asserted-by":"publisher","unstructured":"Rahman F, Devanbu P (2013) How, and why, process metrics are better. In: 2013 35th international conference on software engineering (ICSE), pp 432\u2013441. https:\/\/doi.org\/10.1109\/ICSE.2013.6606589","DOI":"10.1109\/ICSE.2013.6606589"},{"key":"9930_CR35","doi-asserted-by":"publisher","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) Why should I trust you?: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, in KDD \u201916. Association for Computing Machinery, New York, NY, USA, pp 1135\u20131144. https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"issue":"3","key":"9930_CR36","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1049\/iet-sen.2019.0278","volume":"14","author":"K Zhu","year":"2020","unstructured":"Zhu K, Zhang N, Ying S, Zhu D (2020) Within-project and cross-project just-in-time defect prediction based on denoising autoencoder and convolutional neural network. IET Softw 14(3):185\u2013195. https:\/\/doi.org\/10.1049\/iet-sen.2019.0278","journal-title":"IET Softw"},{"issue":"5","key":"9930_CR37","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. Empir Softw Eng 21(5):2072\u20132106. https:\/\/doi.org\/10.1007\/s10664-015-9400-x","journal-title":"Empir Softw Eng"},{"issue":"7","key":"9930_CR38","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1007\/s10664-022-10185-8","volume":"27","author":"T Zhang","year":"2022","unstructured":"Zhang T, Yu Y, Mao X, Lu Y, Li Z, Wang H (2022) FENSE: a feature-based ensemble modeling approach to cross-project just-in-time defect prediction. Empir Softw Eng 27(7):162. https:\/\/doi.org\/10.1007\/s10664-022-10185-8","journal-title":"Empir Softw Eng"},{"key":"9930_CR39","doi-asserted-by":"publisher","first-page":"108852","DOI":"10.1016\/j.knosys.2022.108852","volume":"248","author":"W Zhuang","year":"2022","unstructured":"Zhuang W, Wang H, Zhang X (2022) Just-in-time defect prediction based on AST change embedding. Knowl-Based Syst 248:108852. https:\/\/doi.org\/10.1016\/j.knosys.2022.108852","journal-title":"Knowl-Based Syst"},{"key":"9930_CR40","unstructured":"Papers with code - GloVe: global vectors for word representation. https:\/\/paperswithcode.com\/paper\/glove-global-vectors-for-word-representation. Accessed 13 Aug 2023"},{"key":"9930_CR41","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/978-3-031-37231-5_8","volume-title":"Software technologies. Communications in computer and information science","author":"T Aladics","year":"2023","unstructured":"Aladics T, Heged\u0171s P, Ferenc R (2023) An AST-based code change representation and\u00a0its performance in\u00a0just-in-time vulnerability prediction. In: Fill H-G, van Sinderen M, Maciaszek LA (eds) Software technologies. Communications in computer and information science. Springer, Cham, pp 169\u2013186. https:\/\/doi.org\/10.1007\/978-3-031-37231-5_8"},{"key":"9930_CR42","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.aiopen.2021.08.002","volume":"2","author":"X Han","year":"2021","unstructured":"Han X et al (2021) Pre-trained models: past, present and future. AI Open 2:225\u2013250. https:\/\/doi.org\/10.1016\/j.aiopen.2021.08.002","journal-title":"AI Open"},{"key":"9930_CR43","unstructured":"The dataset of ESEC\/FSE 2023 paper titled \u2018CCT5: a code-change-oriented pre-trained model\u2019|Zenodo. https:\/\/www.zenodo.org\/record\/7998509\/. Accessed 14 Aug 2023"},{"key":"9930_CR44","doi-asserted-by":"publisher","unstructured":"Ni C, Wang W, Yang K, Xia X, Liu K, Lo D (2022) The best of both worlds: integrating semantic features with expert features for defect prediction and localization. In: Proceedings of the 30th ACM joint European software engineering conference and symposium on the foundations of software engineering. In: ESEC\/FSE 2022. Association for Computing Machinery, New York, NY, USA, pp 672\u2013683. https:\/\/doi.org\/10.1145\/3540250.3549165","DOI":"10.1145\/3540250.3549165"},{"key":"9930_CR45","doi-asserted-by":"publisher","unstructured":"Shi E, et al (2023) Towards efficient fine-tuning of pre-trained code models: an experimental study and beyond. In: Proceedings of the 32nd ACM SIGSOFT international symposium on software testing and analysis, in ISSTA 2023. Association for Computing Machinery, New York, NY, USA, pp 39\u201351. https:\/\/doi.org\/10.1145\/3597926.3598036","DOI":"10.1145\/3597926.3598036"},{"issue":"4","key":"9930_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1082983.1083147","volume":"30","author":"J \u015aliwerski","year":"2005","unstructured":"\u015aliwerski J, Zimmermann T, Zeller A (2005) When do changes induce fixes? SIGSOFT Softw Eng Notes 30(4):1\u20135. https:\/\/doi.org\/10.1145\/1082983.1083147","journal-title":"SIGSOFT Softw Eng Notes"},{"key":"9930_CR47","doi-asserted-by":"publisher","unstructured":"Kim S, Zimmermann T, Pan K, Whitehead EJ Jr (2006) Automatic identification of bug-introducing changes. In: Proceedings of the 21st IEEE\/ACM international conference on automated software engineering, in ASE \u201906. IEEE Computer Society, USA, pp 81\u201390. https:\/\/doi.org\/10.1109\/ASE.2006.23","DOI":"10.1109\/ASE.2006.23"},{"issue":"7","key":"9930_CR48","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1109\/TSE.2016.2616306","volume":"43","author":"DA da Costa","year":"2017","unstructured":"da Costa DA, McIntosh S, Shang W, Kulesza U, Coelho R, Hassan AE (2017) A framework for evaluating the results of the SZZ approach for identifying bug-introducing changes. IEEE Trans Softw Eng 43(7):641\u2013657. https:\/\/doi.org\/10.1109\/TSE.2016.2616306","journal-title":"IEEE Trans Softw Eng"},{"key":"9930_CR49","doi-asserted-by":"publisher","unstructured":"Neto EC, da Costa DA, Kulesza U (2018) The impact of refactoring changes on the SZZ algorithm: an empirical study. In: 2018 IEEE 25th international conference on software analysis, evolution and reengineering (SANER), pp 380\u2013390. https:\/\/doi.org\/10.1109\/SANER.2018.8330225","DOI":"10.1109\/SANER.2018.8330225"},{"issue":"8","key":"9930_CR50","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1109\/TSE.2019.2929761","volume":"47","author":"Y Fan","year":"2021","unstructured":"Fan Y, Xia X, da Costa DA, Lo D, Hassan AE, Li S (2021) The impact of mislabeled changes by SZZ on just-in-time defect prediction. IEEE Trans Softw Eng 47(8):1559\u20131586. https:\/\/doi.org\/10.1109\/TSE.2019.2929761","journal-title":"IEEE Trans Softw Eng"},{"issue":"1","key":"9930_CR51","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2021","unstructured":"Zhuang F et al (2021) A comprehensive survey on transfer learning. Proc IEEE 109(1):43\u201376. https:\/\/doi.org\/10.1109\/JPROC.2020.3004555","journal-title":"Proc IEEE"},{"key":"9930_CR52","doi-asserted-by":"crossref","unstructured":"Niu C, Li C, Ng V, Chen D, Ge J, Luo B (2023) An empirical comparison of pre-trained models of source code. arXiv. http:\/\/arxiv.org\/abs\/2302.04026. Accessed 26 Aug 2023","DOI":"10.1109\/ICSE48619.2023.00180"},{"key":"9930_CR53","doi-asserted-by":"publisher","unstructured":"Wang D, et al (2022) Bridging pre-trained models and downstream tasks for source code understanding. In: Proceedings of the 44th international conference on software engineering, in ICSE \u201922. Association for Computing Machinery, New York, NY, USA, pp 287\u2013298. https:\/\/doi.org\/10.1145\/3510003.3510062","DOI":"10.1145\/3510003.3510062"},{"key":"9930_CR54","doi-asserted-by":"publisher","unstructured":"Karmakar A, Robbes R (2021) What do pre-trained code models know about code?. In: 2021 36th IEEE\/ACM international conference on automated software engineering (ASE), pp 1332\u20131336. https:\/\/doi.org\/10.1109\/ASE51524.2021.9678927","DOI":"10.1109\/ASE51524.2021.9678927"},{"key":"9930_CR55","doi-asserted-by":"publisher","unstructured":"Tufano R, Masiero S, Mastropaolo A, Pascarella L, Poshyvanyk D, Bavota G (2022) Using pre-trained models to boost code review automation. In: Proceedings of the 44th international conference on software engineering, in ICSE \u201922. Association for Computing Machinery, New York, NY, USA, pp 2291\u20132302. https:\/\/doi.org\/10.1145\/3510003.3510621","DOI":"10.1145\/3510003.3510621"},{"key":"9930_CR56","unstructured":"Zhou Y, Liu S, Siow J, Du X, Liu Y (2019) Devign: effective vulnerability identification by learning comprehensive program semantics via graph neural networks. In: Proceedings of the 33rd international conference on neural information processing systems. Curran Associates Inc., Red Hook, NY, USA, pp 10197\u201310207"},{"key":"9930_CR57","doi-asserted-by":"publisher","unstructured":"Nafi KW, Kar TS, Roy B, Roy CK, Schneider KA (2019) CLCDSA: cross language code clone detection using syntactical features and API documentation. In: 2019 34th IEEE\/ACM international conference on automated software engineering (ASE), pp 1026\u20131037. https:\/\/doi.org\/10.1109\/ASE.2019.00099","DOI":"10.1109\/ASE.2019.00099"},{"issue":"4","key":"9930_CR58","doi-asserted-by":"publisher","first-page":"19:1","DOI":"10.1145\/3340544","volume":"28","author":"M Tufano","year":"2019","unstructured":"Tufano M, Watson C, Bavota G, Penta MD, White M, Poshyvanyk D (2019) An empirical study on learning bug-fixing patches in the wild via neural machine translation. ACM Trans Softw Eng Methodol 28(4):19:1-19:29. https:\/\/doi.org\/10.1145\/3340544","journal-title":"ACM Trans Softw Eng Methodol"},{"key":"9930_CR59","doi-asserted-by":"publisher","unstructured":"Feng Z, et al (2020) CodeBERT: a pre-trained model for programming and natural languages. In: Findings of the Association for Computational Linguistics: EMNLP 2020, Online: Association for Computational Linguistics, pp 1536\u20131547. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.139","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"9930_CR60","doi-asserted-by":"publisher","unstructured":"Liu Z, Lin W, Shi Y, Zhao J (2021) A robustly optimized BERT pre-training approach with post-training. In: Chinese computational linguistics: 20th China national conference, CCL 2021, Hohhot, China, August 13\u201315, 2021, proceedings. Springer, Berlin, pp 471\u2013484. https:\/\/doi.org\/10.1007\/978-3-030-84186-7_31","DOI":"10.1007\/978-3-030-84186-7_31"},{"key":"9930_CR61","doi-asserted-by":"publisher","unstructured":"Zeng Z, Tan H, Zhang H, Li J, Zhang Y, Zhang L (2022) An extensive study on pre-trained models for program understanding and generation. In: Proceedings of the 31st ACM SIGSOFT international symposium on software testing and analysis, in ISSTA 2022. Association for Computing Machinery, New York, NY, USA, pp 39\u201351. https:\/\/doi.org\/10.1145\/3533767.3534390.","DOI":"10.1145\/3533767.3534390"},{"key":"9930_CR62","doi-asserted-by":"publisher","unstructured":"de Sousa NT, Hasselbring W (2021) JavaBERT: training a transformer-based model for the Java programming language. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2110.10404","DOI":"10.48550\/arXiv.2110.10404"},{"key":"9930_CR63","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the Association for Computational Linguistics: human language technologies, volume 1 (long and short papers), Minneapolis, Minnesota. Association for Computational Linguistics, pp 4171\u20134186. https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"issue":"1","key":"9930_CR64","first-page":"140:5485","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel C et al (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 21(1):140:5485-140:5551","journal-title":"J Mach Learn Res"},{"key":"9930_CR65","doi-asserted-by":"publisher","unstructured":"Automating code review activities by large-scale pre-training|Proceedings of the 30th ACM joint European software engineering conference and symposium on the foundations of software engineering. https:\/\/doi.org\/10.1145\/3540250.3549081. Accessed 15 Aug 2023","DOI":"10.1145\/3540250.3549081"},{"key":"9930_CR66","doi-asserted-by":"publisher","unstructured":"Sennrich R, Haddow B, Birch A (2016) Neural machine translation of rare words with subword units. In: Proceedings of the 54th annual meeting of the Association for Computational Linguistics (volume 1: long papers), Berlin, Germany. Association for Computational Linguistics, pp 1715\u20131725. https:\/\/doi.org\/10.18653\/v1\/P16-1162","DOI":"10.18653\/v1\/P16-1162"},{"key":"9930_CR67","unstructured":"Japanese and Korean voice search|IEEE conference publication|IEEE Xplore. https:\/\/ieeexplore.ieee.org\/document\/6289079. Accessed 27 Aug 2023"},{"key":"9930_CR68","doi-asserted-by":"publisher","unstructured":"Kudo T (2018) Subword regularization: improving neural network translation models with multiple subword candidates. In: Proceedings of the 56th annual meeting of the Association for Computational Linguistics (volume 1: long papers), Melbourne, Australia. Association for Computational Linguistics, pp 66\u201375. https:\/\/doi.org\/10.18653\/v1\/P18-1007","DOI":"10.18653\/v1\/P18-1007"},{"key":"9930_CR69","doi-asserted-by":"publisher","unstructured":"Kudo T, Richardson J (2018) SentencePiece: a simple and language independent subword tokenizer and detokenizer for neural text processing. In: Proceedings of the 2018 conference on empirical methods in natural language processing: system demonstrations, Brussels, Belgium. Association for Computational Linguistics, pp 66\u201371. https:\/\/doi.org\/10.18653\/v1\/D18-2012","DOI":"10.18653\/v1\/D18-2012"},{"key":"9930_CR70","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.aiopen.2022.10.001","volume":"3","author":"T Lin","year":"2022","unstructured":"Lin T, Wang Y, Liu X, Qiu X (2022) A survey of transformers. AI Open 3:111\u2013132. https:\/\/doi.org\/10.1016\/j.aiopen.2022.10.001","journal-title":"AI Open"},{"key":"9930_CR71","unstructured":"Zaheer M, et al (2020) Big bird: transformers for longer sequences. In: Proceedings of the 34th international conference on neural information processing systems, in NIPS\u201920. Curran Associates Inc., Red Hook, NY, USA, pp 17283\u201317297."},{"key":"9930_CR72","unstructured":"Katharopoulos A, Vyas A, Pappas N, Fleuret F (2020) Transformers are RNNs: fast autoregressive transformers with linear attention. In: Proceedings of the 37th international conference on machine learning, PMLR, pp 5156\u20135165. https:\/\/proceedings.mlr.press\/v119\/katharopoulos20a.html. Accessed 28 Aug 2023"},{"key":"9930_CR73","unstructured":"Zhu C, et al (2023) Long-short transformer: efficient transformers for language and vision. In: Advances in neural information processing systems. Curran Associates, Inc., pp 17723\u201317736. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2021\/hash\/9425be43ba92c2b4454ca7bf602efad8-Abstract.html. Accessed 24 Apr 2023"},{"key":"9930_CR74","unstructured":"Bertsch A, Alon U, Neubig G, Gormley MR (2023) Unlimiformer: long-range transformers with unlimited length input. arXiv. http:\/\/arxiv.org\/abs\/2305.01625. Accessed 29 May 2023"},{"key":"9930_CR75","doi-asserted-by":"publisher","unstructured":"LSG Attention: extrapolation of\u00a0pretrained transformers to\u00a0long sequences|SpringerLink. https:\/\/doi.org\/10.1007\/978-3-031-33374-3_35. Accessed 15 Aug 2023","DOI":"10.1007\/978-3-031-33374-3_35"},{"key":"9930_CR76","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/978-3-031-09357-9_3","volume-title":"Security in computer and information sciences","author":"I Kalouptsoglou","year":"2022","unstructured":"Kalouptsoglou I, Siavvas M, Kehagias D, Chatzigeorgiou A, Ampatzoglou A (2022) An empirical evaluation of\u00a0the\u00a0usefulness of\u00a0word embedding techniques in\u00a0deep learning-based vulnerability prediction. In: Gelenbe E, Jankovic M, Kehagias D, Marton A, Vilmos A (eds) Security in computer and information sciences. Springer, Cham, pp 23\u201337. https:\/\/doi.org\/10.1007\/978-3-031-09357-9_3"},{"key":"9930_CR77","doi-asserted-by":"publisher","unstructured":"Ngoc HN, Viet HN, Uehara T (2021) An extended benchmark system of word embedding methods for vulnerability detection. In: Proceedings of the 4th international conference on future networks and distributed systems, in ICFNDS \u201920. Association for Computing Machinery, New York, NY, USA, pp 1\u20138. https:\/\/doi.org\/10.1145\/3440749.3442661","DOI":"10.1145\/3440749.3442661"},{"key":"9930_CR78","doi-asserted-by":"publisher","unstructured":"Zhang Z, et al (2024) Unifying the perspectives of NLP and software engineering: a survey on language models for code. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2311.07989","DOI":"10.48550\/arXiv.2311.07989"},{"key":"9930_CR79","unstructured":"Hu EJ, et al (2022) LoRA: low-rank adaptation of large language models. In: Presented at the ICLR 2022. https:\/\/www.microsoft.com\/en-us\/research\/publication\/lora-low-rank-adaptation-of-large-language-models\/. Accessed 15 Aug 2023"},{"key":"9930_CR80","doi-asserted-by":"publisher","first-page":"842","DOI":"10.1162\/tacl_a_00349","volume":"8","author":"A Rogers","year":"2021","unstructured":"Rogers A, Kovaleva O, Rumshisky A (2021) A primer in BERTology: what we know about how BERT works. Trans Assoc Comput Linguist 8:842\u2013866. https:\/\/doi.org\/10.1162\/tacl_a_00349","journal-title":"Trans Assoc Comput Linguist"},{"key":"9930_CR81","doi-asserted-by":"publisher","unstructured":"Kovaleva O, Romanov A, Rogers A, Rumshisky A (2019) Revealing the dark secrets of BERT. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), Hong Kong, China. Association for Computational Linguistics, pp 4365\u20134374. https:\/\/doi.org\/10.18653\/v1\/D19-1445","DOI":"10.18653\/v1\/D19-1445"},{"key":"9930_CR82","doi-asserted-by":"publisher","unstructured":"Hao Y, Dong L, Wei F, Xu K (2019) Visualizing and understanding the effectiveness of BERT. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), Hong Kong, China. Association for Computational Linguistics, pp 4143\u20134152. https:\/\/doi.org\/10.18653\/v1\/D19-1424","DOI":"10.18653\/v1\/D19-1424"},{"key":"9930_CR83","doi-asserted-by":"publisher","unstructured":"Howard J, Ruder S (2018) Universal language model fine-tuning for text classification. In: Proceedings of the 56th annual meeting of the Association for Computational Linguistics (volume 1: long papers), Melbourne, Australia. Association for Computational Linguistics, pp 328\u2013339. https:\/\/doi.org\/10.18653\/v1\/P18-1031","DOI":"10.18653\/v1\/P18-1031"},{"key":"9930_CR84","unstructured":"Shazeer N, Stern M (2018) Adafactor: adaptive learning rates with sublinear memory cost. In: Proceedings of the 35th international conference on machine learning, PMLR, pp 4596\u20134604. https:\/\/proceedings.mlr.press\/v80\/shazeer18a.html. Accessed 21 Sept 2023"},{"issue":"1","key":"9930_CR85","doi-asserted-by":"publisher","first-page":"43","DOI":"10.2478\/pralin-2018-0002","volume":"110","author":"M Popel","year":"2018","unstructured":"Popel M, Bojar O (2018) Training tips for the transformer model. Prague Bull Math Linguist 110(1):43\u201370. https:\/\/doi.org\/10.2478\/pralin-2018-0002","journal-title":"Prague Bull Math Linguist"},{"key":"9930_CR86","doi-asserted-by":"publisher","unstructured":"Improving transformer optimization through better initialization|Proceedings of the 37th international conference on machine learning. https:\/\/doi.org\/10.5555\/3524938.3525354. Accessed 21 Sept 2023","DOI":"10.5555\/3524938.3525354"},{"key":"9930_CR87","doi-asserted-by":"crossref","unstructured":"Mahbub P, Shuvo O, Rahman MM (2023) Defectors: a large, diverse Python dataset for defect prediction. arXiv. http:\/\/arxiv.org\/abs\/2303.04738. Accessed 03 Jun 2023","DOI":"10.1109\/MSR59073.2023.00085"},{"key":"9930_CR88","doi-asserted-by":"publisher","unstructured":"Fu Z, Yang H, So AM-C, Lam W, Bing L, Collier N (2023) On the effectiveness of parameter-efficient fine-tuning. In: Proceedings of the AAAI conference on artificial intelligence, vol 37, no 11, Art no 11. https:\/\/doi.org\/10.1609\/aaai.v37i11.26505","DOI":"10.1609\/aaai.v37i11.26505"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09930-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09930-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09930-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T10:10:30Z","timestamp":1724494230000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09930-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":88,"journal-issue":{"issue":"27","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["9930"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09930-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,3]]},"assertion":[{"value":"19 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}