{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T11:52:53Z","timestamp":1769773973208,"version":"3.49.0"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032159830","type":"print"},{"value":"9783032159847","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-15984-7_24","type":"book-chapter","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:35:03Z","timestamp":1769718903000},"page":"347-361","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pre-trained Code Language Models for\u00a0Just-in-Time Software Defect Prediction: An Empirical Study"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9620-6455","authenticated-orcid":false,"given":"Monique Louise","family":"Monteiro","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2831-4274","authenticated-orcid":false,"given":"George Gomes","family":"Cabral","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5614-229X","authenticated-orcid":false,"given":"Adriano Lorena","family":"de Oliveira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"issue":"27","key":"24_CR1","doi-asserted-by":"publisher","first-page":"16911","DOI":"10.1007\/s00521-024-09930-5","volume":"36","author":"M Abu Talib","year":"2024","unstructured":"Abu Talib, M., Bou Nassif, A., Azzeh, M., Alesh, Y., Afadar, Y.: Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction. Neural Comput. Appl. 36(27), 16911\u201316940 (2024). https:\/\/doi.org\/10.1007\/s00521-024-09930-5","journal-title":"Neural Comput. Appl."},{"key":"24_CR2","doi-asserted-by":"publisher","unstructured":"Cabral, G.G., Minku, L.L., Oliveira, A.L., Pessoa, D.A., Tabassum, S.: An investigation of online and offline learning models for online just-in-time software defect prediction. Empirical Softw. Eng. 28(5) (2023). https:\/\/doi.org\/10.1007\/s10664-023-10335-6","DOI":"10.1007\/s10664-023-10335-6"},{"key":"24_CR3","doi-asserted-by":"publisher","unstructured":"Cabral, G.G., Minku, L.L., Shihab, E., Mujahid, S.: 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 (2019). https:\/\/doi.org\/10.1109\/ICSE.2019.00076","DOI":"10.1109\/ICSE.2019.00076"},{"key":"24_CR4","doi-asserted-by":"publisher","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785\u2013794. ACM, New York, NY, USA (2016). https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"24_CR5","doi-asserted-by":"publisher","unstructured":"Chen, X., Xu, F., Huang, Y., Zhang, N., Zheng, Z.: Jit-smart: a multi-task learning framework for just-in-time defect prediction and localization. Proc. ACM Softw. Eng. 1(FSE) (2024). https:\/\/doi.org\/10.1145\/3643727","DOI":"10.1145\/3643727"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Condevaux, C., Harispe, S.: Lsg attention: extrapolation of pretrained transformers to long sequences. In: Kashima, H., Ide, T., Peng, W.C. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 443\u2013454. Springer, Cham (2023)","DOI":"10.1007\/978-3-031-33374-3_35"},{"key":"24_CR7","doi-asserted-by":"publisher","unstructured":"Feng, Z., et al.: CodeBERT: a pre-trained model for programming and natural languages. In: Cohn, T., He, Y., Liu, Y. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2020. pp. 1536\u20131547. ACL, November 2020. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.139","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"24_CR8","unstructured":"Grattafiori, A., et\u00a0al.: The llama 3 herd of models (2024). https:\/\/arxiv.org\/abs\/2407.21783"},{"key":"24_CR9","doi-asserted-by":"publisher","unstructured":"Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., Yin, J.: UniXcoder: unified cross-modal pre-training for code representation. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7212\u20137225. ACL, Dublin, Ireland, May 2022. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.499, https:\/\/aclanthology.org\/2022.acl-long.499\/","DOI":"10.18653\/v1\/2022.acl-long.499"},{"key":"24_CR10","unstructured":"Guo, Y., Gao, X., Zhang, Z., Chan, W.K., Jiang, B.: A study on the impact of pre-trained model on just-in-time defect prediction (2023). https:\/\/arxiv.org\/abs\/2309.02317"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Herbold, S., et\u00a0al.: A fine-grained data set and analysis of tangling in bug fixing commits. Empirical Softw. Eng. 27 (2020). https:\/\/doi.org\/10.1007\/s10664-021-10083-5","DOI":"10.1007\/s10664-021-10083-5"},{"key":"24_CR12","doi-asserted-by":"publisher","unstructured":"Hoang, T., Kang, H.J., Lo, D., Lawall, J.: Cc2vec: distributed representations of code changes. In: Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering, ICSE 2020, pp. 518\u2013529. ACM, New York, NY, USA (2020). https:\/\/doi.org\/10.1145\/3377811.3380361","DOI":"10.1145\/3377811.3380361"},{"key":"24_CR13","unstructured":"Houlsby, N., et al.: Parameter-efficient transfer learning for NLP. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a097, pp. 2790\u20132799. PMLR, 09\u201315 June 2019"},{"key":"24_CR14","unstructured":"Hu, E.J., et al.: LoRA: low-rank adaptation of large language models. In: The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, 25\u201329 April 2022. OpenReview.net (2022)"},{"issue":"6","key":"24_CR15","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.: A large-scale empirical study of just-in-time quality assurance. IEEE Trans. Software Eng. 39(6), 757\u2013773 (2013). https:\/\/doi.org\/10.1109\/TSE.2012.70","journal-title":"IEEE Trans. Software Eng."},{"key":"24_CR16","doi-asserted-by":"publisher","unstructured":"Keshavarz, H., Nagappan, M.: Apachejit: a large dataset for just-in-time defect prediction. In: Proceedings of the 19th International Conference on Mining Software Repositories, MSR 2022, pp. 191\u2013195. ACM, New York, NY, USA (2022). https:\/\/doi.org\/10.1145\/3524842.3527996","DOI":"10.1145\/3524842.3527996"},{"key":"24_CR17","doi-asserted-by":"publisher","unstructured":"Li, Z., et al.: Automating code review activities by large-scale pre-training. In: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC\/FSE 2022, pp. 1035\u20131047. ACM, New York, NY, USA (2022). https:\/\/doi.org\/10.1145\/3540250.3549081","DOI":"10.1145\/3540250.3549081"},{"key":"24_CR18","doi-asserted-by":"publisher","unstructured":"Lin, B., Wang, S., Liu, Z., Liu, Y., Xia, X., Mao, X.: CCT5: a code-change-oriented pre-trained model. In: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC\/FSE 2023, pp. 1509\u20131521. ACM, New York, NY, USA (2023). https:\/\/doi.org\/10.1145\/3611643.3616339","DOI":"10.1145\/3611643.3616339"},{"key":"24_CR19","doi-asserted-by":"publisher","unstructured":"Liu, S., Keung, J., Yang, Z., Liu, F., Zhou, Q., Liao, Y.: Delving into parameter-efficient fine-tuning in code change learning: an empirical study. In: 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 465\u2013476 (2024). https:\/\/doi.org\/10.1109\/SANER60148.2024.00055","DOI":"10.1109\/SANER60148.2024.00055"},{"key":"24_CR20","doi-asserted-by":"publisher","unstructured":"Ni, C., Wang, W., Yang, K., Xia, X., Liu, K., Lo, D.: 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, ESEC\/FSE 2022, pp. 672\u2013683. ACM, New York, NY, USA (2022). https:\/\/doi.org\/10.1145\/3540250.3549165","DOI":"10.1145\/3540250.3549165"},{"key":"24_CR21","unstructured":"OpenAI: Gpt-4 technical report (2024). https:\/\/arxiv.org\/abs\/2303.08774"},{"key":"24_CR22","unstructured":"OpenAI: GPT-4o system card (2024). https:\/\/arxiv.org\/abs\/2410.21276"},{"key":"24_CR23","unstructured":"OpenAI: OpenAI o1 system card (2024). https:\/\/arxiv.org\/abs\/2412.16720"},{"key":"24_CR24","unstructured":"OpenAI: OpenAI o3 and o4-mini system card (2025). https:\/\/cdn.openai.com\/pdf\/2221c875-02dc-4789-800b-e7758f3722c1\/o3-and-o4-mini-system-card.pdf"},{"key":"24_CR25","doi-asserted-by":"publisher","unstructured":"Pornprasit, C., Tantithamthavorn, C.: Jitline: a simpler, better, faster, finer-grained just-in-time defect prediction. In: Blincoe, K., Nagappan, M. (eds.) Proceedings - 2021 IEEE\/ACM 18th International Conference on Mining Software Repositories, MSR 2021. pp. 369\u2013379. Proceedings - 2021 IEEE\/ACM 18th International Conference on Mining Software Repositories, MSR 2021, IEEE, Institute of Electrical and Electronics Engineers, United States of America (2021). https:\/\/doi.org\/10.1109\/MSR52588.2021.00049","DOI":"10.1109\/MSR52588.2021.00049"},{"key":"24_CR26","unstructured":"Rozi\u00e8re, B., et al.: Code llama: open foundation models for code (2024). https:\/\/arxiv.org\/abs\/2308.12950"},{"key":"24_CR27","doi-asserted-by":"crossref","unstructured":"de\u00a0Sousa, N.T., Hasselbring, W.: JavaBERT: training a transformer-based model for the java programming language (2021)","DOI":"10.1109\/ASEW52652.2021.00028"},{"key":"24_CR28","unstructured":"Vaswani, A., et al.: Attention is all you need (2023). https:\/\/arxiv.org\/abs\/1706.03762"},{"issue":"1","key":"24_CR29","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1007\/s44196-024-00551-3","volume":"17","author":"X Wang","year":"2024","unstructured":"Wang, X., Lu, L., Yang, Z., Tian, Q., Lin, H.: Parameter-efficient multi-classification software defect detection method based on pre-trained LLMs. Int. J. Comput. Intell. Syst. 17(1), 152 (2024). https:\/\/doi.org\/10.1007\/s44196-024-00551-3","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"24_CR30","doi-asserted-by":"publisher","unstructured":"Wang, Y., Le, H., Gotmare, A., Bui, N., Li, J., Hoi, S.: CodeT5+: open code large language models for code understanding and generation. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 1069\u20131088. ACL, Singapore, December 2023. https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.68","DOI":"10.18653\/v1\/2023.emnlp-main.68"},{"key":"24_CR31","unstructured":"Warner, B., et al.: Smarter, better, faster, longer: a modern bidirectional encoder for fast, memory efficient, and long context finetuning and inference (2024). https:\/\/arxiv.org\/abs\/2412.13663"},{"key":"24_CR32","doi-asserted-by":"publisher","unstructured":"Zeng, Z., Zhang, Y., Zhang, H., Zhang, L.: Deep just-in-time defect prediction: how far are we? In: Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2021, pp. 427\u2013438. ACM, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3460319.3464819","DOI":"10.1145\/3460319.3464819"},{"issue":"7","key":"24_CR33","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.: Fense: a feature-based ensemble modeling approach to cross-project just-in-time defect prediction. Empir. Softw. Eng. 27(7), 162 (2022). https:\/\/doi.org\/10.1007\/s10664-022-10185-8","journal-title":"Empir. Softw. Eng."},{"key":"24_CR34","unstructured":"Zhao, Y., Chen, H.: Deep incremental learning of imbalanced data for just-in-time software defect prediction (2023). https:\/\/arxiv.org\/abs\/2310.12289"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-15984-7_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:35:09Z","timestamp":1769718909000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-15984-7_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032159830","9783032159847"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-15984-7_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"30 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that\u00a0are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fortaleza-CE","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bracis.sbc.org.br\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}