{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T04:03:28Z","timestamp":1781841808309,"version":"3.54.5"},"reference-count":77,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IIEEE Trans. Software Eng."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1109\/tse.2024.3428324","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T17:56:09Z","timestamp":1721066169000},"page":"2240-2253","source":"Crossref","is-referenced-by-count":19,"title":["Towards Efficient Fine-Tuning of Language Models With Organizational Data for Automated Software Review"],"prefix":"10.1109","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7580-5757","authenticated-orcid":false,"given":"Mona","family":"Nashaat","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Port Said University, Port Said, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5095-3000","authenticated-orcid":false,"given":"James","family":"Miller","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/tse.2022.3178469"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/tse.2023.3348172"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3585004"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109450"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3463274.3463336"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/SANER48275.2020.9054794"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-05559-3"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-16145-3_25"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014910"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/saner.2018.8330261"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510621"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549099"},{"key":"ref13","article-title":"On the feasibility of specialized ability stealing for large language code models","author":"Li","year":"2023"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.scico.2021.102652"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549081"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2023.3308952"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3551349.3556955"},{"key":"ref18","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wei","year":"2022"},{"key":"ref19","article-title":"PEFT: State-of-the-art parameter-efficient fine-tuning methods","author":"Mangrulkar","year":"2022"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1515\/fcds-2016-0004"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s10844-017-0484-1"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s11390-019-1917-9"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/seaa51224.2020.00085"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3639478.3643111"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-022-10205-7"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549119"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3605943"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01653-1"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.5339\/connect.2023.2"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58539-6_34"},{"key":"ref31","first-page":"16857","article-title":"MPNet: Masked and permuted pre-training for language understanding","volume-title":"Proc. Adv. Neural Inf. Process. Syst","author":"Song","year":"2020"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3520312.3534862"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/sp46215.2023.10179324"},{"key":"ref34","article-title":"Challenges and applications of large language models","author":"Kaddour","year":"2023"},{"key":"ref35","article-title":"word2vec parameter learning explained","author":"Rong","year":"2014"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2900753"},{"key":"ref38","article-title":"Large scale legal text classification using transformer models","author":"Shaheen","year":"2020"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3447541"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d15-1166"},{"key":"ref41","first-page":"1243","article-title":"Convolutional sequence to sequence learning","volume-title":"Proc. 34th Int. Conf. Mach. Learn. (ICML\u201917)","volume":"70","author":"Gehring","year":"2017"},{"key":"ref42","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2019"},{"key":"ref43","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","author":"Liu","year":"2019"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/s11023-020-09548-1"},{"key":"ref45","first-page":"26106","article-title":"LEVER: Learning to verify language-to-code generation with execution","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Ni","year":"2023"},{"key":"ref46","article-title":"No need to lift a finger anymore? Assessing the quality of code generation by ChatGPT","author":"Liu","year":"2023"},{"key":"ref47","article-title":"Summarization is (almost) dead","author":"Pu","year":"2023"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3551349.3559555"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00027"},{"key":"ref50","article-title":"Self-planning code generation with large language model","author":"Jiang","year":"2023"},{"key":"ref51","article-title":"Self-instruct: Aligning language model with self generated instructions","author":"Wang","year":"2022"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3385188"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3473910"},{"key":"ref54","article-title":"LoRA: Low-rank adaptation of large language models","author":"Hu","year":"2021"},{"key":"ref55","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Ouyang","year":"2022"},{"key":"ref56","article-title":"Code LLAMA: Open foundation models for code","author":"Roziere","year":"2023"},{"key":"ref57","article-title":"LLAMA: Open and efficient foundation language models","author":"Touvron","year":"2023"},{"key":"ref58","article-title":"A survey of large Language models for code: Evolution, benchmarking, and future trends","author":"Zheng","year":"2024"},{"key":"ref59","article-title":"VisionLLM: Large language model is also an open-ended decoder for vision-centric tasks","author":"Wang","year":"2023"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE59848.2023.00026"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.3390\/app12125854"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/HASE.2015.45"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3069248"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.3390\/app13158788"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.685"},{"key":"ref66","article-title":"Learning deep semantic model for code search using CodeSearchNet corpus","author":"Wu","year":"2022"},{"key":"ref67","article-title":"CodeXGLUE: A machine learning benchmark dataset for code understanding and generation","author":"Lu","year":"2021"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.3115\/1073083.1073135"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1145\/3522674"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/ASE56229.2023.00193"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1145\/3611643.3613892"},{"key":"ref73","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Brown","year":"2020"},{"issue":"2","key":"ref74","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1049\/sfw2.12097","article-title":"Constructing meaningful code changes via graph transformer","volume":"17","author":"Guo","year":"2023","journal-title":"IET Softw."},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2023.3324613"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1145\/3611643.3617850"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1145\/3597503.3623306"}],"container-title":["IEEE Transactions on Software Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/32\/10683742\/10599336.pdf?arnumber=10599336","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T05:35:23Z","timestamp":1726810523000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10599336\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":77,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tse.2024.3428324","relation":{},"ISSN":["0098-5589","1939-3520","2326-3881"],"issn-type":[{"value":"0098-5589","type":"print"},{"value":"1939-3520","type":"electronic"},{"value":"2326-3881","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9]]}}}