{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T23:48:47Z","timestamp":1768348127765,"version":"3.49.0"},"reference-count":80,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"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":[[2025,11]]},"DOI":"10.1109\/tse.2025.3609876","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T17:33:48Z","timestamp":1758044028000},"page":"3088-3102","source":"Crossref","is-referenced-by-count":1,"title":["An Empirical Study of Exploring the Capabilities of Large Language Models in Code Learning"],"prefix":"10.1109","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5598-4006","authenticated-orcid":false,"given":"Shangqing","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0822-1517","authenticated-orcid":false,"given":"Daya","family":"Guo","sequence":"additional","affiliation":[{"name":"SUN YAT-SEN University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8316-1894","authenticated-orcid":false,"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0044-466X","authenticated-orcid":false,"given":"Wei","family":"Ma","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2263-7383","authenticated-orcid":false,"given":"Yanzhou","family":"Li","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7300-9215","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}]}],"member":"263","reference":[{"key":"ref1","article-title":"ChatGPT: Optimizing language models for dialogue","year":"2022"},{"key":"ref2","article-title":"GitHub copilot","year":"2024"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3650212.3680323"},{"key":"ref4","article-title":"ChatUniTest: A ChatGPT-based automated unit test generation tool","author":"Xie","year":"2023"},{"key":"ref5","article-title":"Generative AI in SDLC: The next frontier of software development"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.499"},{"key":"ref8","article-title":"CodeGen: An open large language model for code with multi-turn program synthesis","author":"Nijkamp","year":"2022"},{"key":"ref9","article-title":"LLaMA: Open and efficient foundation language models","author":"Touvron","year":"2023"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1108\/ws.2000.07949fab.004"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-emnlp.224"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.blackboxnlp-1.31"},{"key":"ref13","article-title":"CodeSearchNet Challenge: Evaluating the state of semantic code search","author":"Husain","year":"2019"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10139"},{"key":"ref15","article-title":"LoRA: Low-rank adaptation of large language models","author":"Hu","year":"2021"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2023.08.012"},{"key":"ref19","article-title":"An empirical study of exploring the capabilities of large language models in code learning","year":"2023"},{"key":"ref20","article-title":"Is self-attention powerful to learn code syntax and semantics?","author":"Ma","year":"2022"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3551349.3556900"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ASE51524.2021.9678927"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510050"},{"key":"ref24","article-title":"AST \u2014 Abstract syntax trees","year":"2025"},{"key":"ref25","article-title":"Uses of package org.eclipse.jdt.core.dom","year":"2025"},{"key":"ref26","first-page":"10197","article-title":"Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Zhou","year":"2019"},{"key":"ref27","first-page":"87","article-title":"Deep learning code fragments for code clone detection","volume-title":"Proc. 31st IEEE\/ACM Int. Conf. Autom. Softw. Eng.","author":"White","year":"2016"},{"key":"ref28","article-title":"Retrieval-augmented generation for code summarization via hybrid GNN","author":"Liu","year":"2020"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00086"},{"key":"ref30","article-title":"GraphCodeBERT: Pre-training code representations with data flow","author":"Guo","year":"2020"},{"key":"ref31","article-title":"Global relational models of source code","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hellendoorn","year":"2019"},{"key":"ref32","article-title":"Structured neural summarization","author":"Fernandes","year":"2018"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00207"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.482"},{"key":"ref35","article-title":"CodeXGLUE: A machine learning benchmark dataset for code understanding and generation","author":"Lu","year":"2021"},{"key":"ref36","first-page":"8696","article-title":"CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process.","author":"Wang","year":"2021"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.703"},{"issue":"11","key":"ref38","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref39","article-title":"Project CodeNet: A large-scale AI for code dataset for learning a diversity of coding tasks","author":"Puri","year":"2021"},{"key":"ref40","article-title":"LLaMA-Adapter: Efficient fine-tuning of language models with zero-init attention","author":"Zhang","year":"2023"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-short.107"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.568"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICSME.2014.77"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.442"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1192"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3022671.2984041"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/MSR.2013.6624029"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.3115\/1220355.1220427"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3417058"},{"key":"ref50","article-title":"Awesome-ChatGPT-prompts","year":"2025"},{"key":"ref51","article-title":"Learning to represent programs with graphs","author":"Allamanis","year":"2017"},{"key":"ref52","first-page":"27865","article-title":"Self-supervised bug detection and repair","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Allamanis","year":"2021"},{"key":"ref53","article-title":"LongCoder: A long-range pre-trained language model for code completion","author":"Guo","year":"2023"},{"key":"ref54","article-title":"PEFT: State-of-the-art parameter-efficient fine-tuning methods","author":"Mangrulkar"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/SANER48275.2020.9054857"},{"key":"ref56","article-title":"BLOOM: A 176b-parameter open-access multilingual language model","author":"Scao","year":"2022"},{"key":"ref57","article-title":"OPT: Open pre-trained transformer language models","author":"Zhang","year":"2022"},{"key":"ref58","article-title":"GLM-130b: An open bilingual pre-trained model","author":"Zeng","year":"2022"},{"key":"ref59","article-title":"CodeGen2: Lessons for training LLMs on programming and natural languages","author":"Nijkamp","year":"2023"},{"key":"ref60","article-title":"The LLaMA 3 herd of models","author":"Dubey","year":"2024"},{"key":"ref61","article-title":"Code LLaMA: Open foundation models for code","author":"Roziere","year":"2023"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.findings-acl.1009"},{"key":"ref63","article-title":"Magicoder: Source code is all you need","author":"Wei","year":"2023"},{"key":"ref64","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018"},{"key":"ref65","article-title":"Emergent abilities of large language models","author":"Wei","year":"2022"},{"key":"ref66","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Brown","year":"2020"},{"key":"ref67","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Wei","year":"2022"},{"issue":"8","key":"ref68","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI Blog"},{"key":"ref69","article-title":"Improving language understanding by generative pre-training","author":"Radford","year":"2018"},{"key":"ref70","article-title":"Scaling language models: Methods, analysis & insights from training gopher","author":"Rae","year":"2021"},{"key":"ref71","article-title":"PaLM: Scaling language modeling with pathways","author":"Chowdhery","year":"2022"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.91"},{"key":"ref73","article-title":"Scaling instruction-finetuned language models","author":"Chung","year":"2022"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1126\/science.abq1158"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1145\/3664606"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1145\/3691620.3695066"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1145\/3714461"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/SANER60148.2024.00055"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE59848.2023.00026"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/ASE56229.2023.00125"}],"container-title":["IEEE Transactions on Software Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/32\/11251265\/11165494.pdf?arnumber=11165494","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T06:15:58Z","timestamp":1763532958000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11165494\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":80,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tse.2025.3609876","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":[[2025,11]]}}}