{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T06:20:09Z","timestamp":1763533209138,"version":"3.45.0"},"reference-count":86,"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"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076121"],"award-info":[{"award-number":["62076121"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Program (JD) of Hubei Province","award":["2023BAA024"],"award-info":[{"award-number":["2023BAA024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IIEEE Trans. Software Eng."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1109\/tse.2025.3605768","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T17:53:28Z","timestamp":1756922008000},"page":"3038-3055","source":"Crossref","is-referenced-by-count":0,"title":["Post-Incorporating Code Structural Knowledge Into Pretrained Models via ICL for Code Translation"],"prefix":"10.1109","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7759-3906","authenticated-orcid":false,"given":"Yali","family":"Du","sequence":"first","affiliation":[{"name":"National Key Laboratory for Novel Software Technology and School of Artificial Intelligence, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4701-5379","authenticated-orcid":false,"given":"Hui","family":"Sun","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology and School of Artificial Intelligence, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7977-5500","authenticated-orcid":false,"given":"Ming","family":"Li","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology and School of Artificial Intelligence, Nanjing University, Nanjing, China"}]}],"member":"263","reference":[{"year":"2024","author":"Guo","article-title":"DeepSeek-Coder: When the large language model meets programming\u2013the Rise of code intelligence","key":"ref1"},{"key":"ref2","article-title":"CodeGen: An open large language model for code with multi-turn program synthesis","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Nijkamp","year":"2022"},{"year":"2023","author":"Roziere","article-title":"Code Llama: Open foundation models for code","key":"ref3"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1108\/ws.2000.07949fab.004"},{"key":"ref5","article-title":"Revisiting chain-of-thought in code generation: Do language models need to learn reasoning before coding?","volume-title":"Proc. 42nd Int. Conf. Mach. Learn.","author":"Liu","year":"2025"},{"year":"2025","author":"Li","article-title":"Enhancing LLMs in long code translation through instrumentation and program state alignment","key":"ref6"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1145\/3597503.3639226"},{"key":"ref8","first-page":"54","article-title":"TreeBERT: A tree-based pre-trained model for programming language","volume-title":"Proc. 37th Conf. Uncertainty Artif. Intell.","author":"Jiang","year":"2021"},{"key":"ref9","first-page":"2552","article-title":"Tree-to-tree neural networks for program translation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Chen","year":"2018"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1145\/3290353"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.24963\/ijcai.2023\/249"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1145\/3611643.3616338"},{"key":"ref13","article-title":"GraphCodeBERT: Pre-training code representations with data flow","volume-title":"Proc. 9th Int. Conf. Learn. Representations","author":"Guo","year":"2021"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1109\/TSE.2020.3038681"},{"key":"ref15","first-page":"15839","article-title":"AST-T5: Structure-aware pretraining for code generation and understanding","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Gong","year":"2024"},{"year":"2024","author":"Luo","article-title":"In-context learning with retrieved demonstrations for language models: A survey","key":"ref16"},{"doi-asserted-by":"publisher","key":"ref17","DOI":"10.18653\/v1\/2022.deelio-1.10"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.1109\/CVPR52688.2022.00501"},{"doi-asserted-by":"publisher","key":"ref19","DOI":"10.18653\/v1\/2021.emnlp-main.517"},{"year":"2024","author":"Li","article-title":"Long-context LLMs struggle with long in-context learning","key":"ref20"},{"doi-asserted-by":"publisher","key":"ref21","DOI":"10.18653\/v1\/2024.naacl-long.17"},{"key":"ref22","article-title":"MoT: Pre-thinking and recalling enable ChatGPT to self-improve with memory-of-thoughts","author":"Li","year":"2023","journal-title":"CoRR"},{"key":"ref23","first-page":"39818","article-title":"Compositional exemplars for in-context learning","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Ye","year":"2023"},{"doi-asserted-by":"publisher","key":"ref24","DOI":"10.18653\/v1\/2023.acl-long.78"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.18653\/v1\/2022.naacl-main.191"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.18653\/v1\/2023.findings-acl.564"},{"doi-asserted-by":"publisher","key":"ref27","DOI":"10.18653\/v1\/2022.emnlp-main.644"},{"doi-asserted-by":"publisher","key":"ref28","DOI":"10.18653\/v1\/2023.acl-long.153"},{"doi-asserted-by":"publisher","key":"ref29","DOI":"10.1287\/moor.3.3.177"},{"year":"2025","author":"Sun","article-title":"MDP3: A training-free approach for list-wise frame selection in video-LLMs","key":"ref30"},{"key":"ref31","article-title":"Unsupervised translation of programming languages","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Rozi\u00e8re","year":"2020"},{"key":"ref32","article-title":"Leveraging automated unit tests for unsupervised code translation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Roziere","year":"2022"},{"key":"ref33","article-title":"Code translation with compiler representations","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Szafraniec","year":"2023"},{"doi-asserted-by":"publisher","key":"ref34","DOI":"10.1145\/3660778"},{"doi-asserted-by":"publisher","key":"ref35","DOI":"10.18653\/v1\/2023.findings-acl.143"},{"key":"ref36","article-title":"CodeXGLUE: A machine learning benchmark dataset for code understanding and generation","volume-title":"Proc. Neural Inf. Process. Syst. Track Datasets Benchmarks","author":"Lu","year":"2021"},{"year":"2024","author":"Yang","article-title":"Qwen2 Technical Report","key":"ref37"},{"year":"2024","author":"Yang","article-title":"Qwen2.5 Technical Report","key":"ref38"},{"year":"2024","author":"Team","article-title":"Gemma 2: Improving open language models at a practical size","key":"ref39"},{"year":"2024","author":"Zhu","article-title":"DeepSeek-Coder-V2: Breaking the barrier of closed-source models in code intelligence","key":"ref40"},{"year":"2023","author":"Touvron","article-title":"Llama: Open and efficient foundation language models","key":"ref41"},{"year":"2023","author":"Touvron","article-title":"Llama 2: Open foundation and fine-tuned chat models","key":"ref42"},{"year":"2023","author":"Achiam","article-title":"GPT-4 technical report","key":"ref43"},{"doi-asserted-by":"publisher","key":"ref44","DOI":"10.1137\/0218082"},{"doi-asserted-by":"publisher","key":"ref45","DOI":"10.3115\/1073083.1073135"},{"year":"2020","author":"Ren","article-title":"CodeBLEU: A method for automatic evaluation of code synthesis","key":"ref46"},{"doi-asserted-by":"publisher","key":"ref47","DOI":"10.1109\/AICCSA47632.2019.9035292"},{"key":"ref48","first-page":"180","article-title":"Automatic inference of java-to-swift translation rules for porting mobile applications","volume-title":"Proc. IEEE\/ACM 5th Int. Conf. Mobile Softw. Eng. Syst. (MOBILESoft)","author":"An","year":"2018"},{"doi-asserted-by":"publisher","key":"ref49","DOI":"10.1109\/IPDPSW.2017.84"},{"doi-asserted-by":"publisher","key":"ref50","DOI":"10.1109\/AIMS52415.2021.9466033"},{"doi-asserted-by":"publisher","key":"ref51","DOI":"10.1145\/2661136.2661148"},{"doi-asserted-by":"publisher","key":"ref52","DOI":"10.1109\/ASE.2015.74"},{"doi-asserted-by":"publisher","key":"ref53","DOI":"10.1145\/2491411.2494584"},{"key":"ref54","first-page":"756","article-title":"Mapping API elements for code migration with vector representations","volume-title":"Proc. IEEE\/ACM 38th Int. Conf. Softw. Eng. Companion (ICSE-C)","author":"Nguyen","year":"2016"},{"doi-asserted-by":"publisher","key":"ref55","DOI":"10.1145\/3212695"},{"doi-asserted-by":"publisher","key":"ref56","DOI":"10.1109\/ASE.2015.36"},{"key":"ref57","article-title":"Unsupervised machine translation using monolingual corpora only","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lample","year":"2018"},{"key":"ref58","first-page":"23685","article-title":"BabelTower: Learning to auto-parallelized program translation","volume-title":"Proc. 39th Int. Conf. Mach. Learn.","author":"Wen","year":"2022"},{"doi-asserted-by":"publisher","key":"ref59","DOI":"10.1145\/3397481.3450656"},{"doi-asserted-by":"publisher","key":"ref60","DOI":"10.1609\/aaai.v36i10.21434"},{"doi-asserted-by":"publisher","key":"ref61","DOI":"10.1109\/ICDM58522.2023.00018"},{"key":"ref62","first-page":"1907","article-title":"A joint learning model with variational interaction for multilingual program translation","volume-title":"Proc. 39th IEEE\/ACM Int. Conf. Automat. Softw. Eng.","author":"Du","year":"2024"},{"doi-asserted-by":"publisher","key":"ref63","DOI":"10.1109\/ICDM.2018.00133"},{"doi-asserted-by":"publisher","key":"ref64","DOI":"10.1109\/ICDM50108.2020.00141"},{"key":"ref65","first-page":"20421","article-title":"Pyramid attention for source code summarization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chai","year":"2022"},{"doi-asserted-by":"publisher","key":"ref66","DOI":"10.1109\/ICSE.2007.30"},{"doi-asserted-by":"publisher","key":"ref67","DOI":"10.1145\/3213846.3213871"},{"year":"2023","article-title":"ChatGPT: Optimizing language models for dialogue","key":"ref68"},{"year":"2024","author":"Dubey","article-title":"The Llama 3 herd of models","key":"ref69"},{"year":"2024","article-title":"Introducing the next generation of Claude","key":"ref70"},{"doi-asserted-by":"publisher","key":"ref71","DOI":"10.1109\/TSE.2024.3382365"},{"doi-asserted-by":"publisher","key":"ref72","DOI":"10.1145\/3650105.3652301"},{"doi-asserted-by":"publisher","key":"ref73","DOI":"10.3233\/faia240968"},{"key":"ref74","first-page":"2448","article-title":"Bridging gaps in LLM code translation: Reducing errors with call graphs and bridged debuggers","volume-title":"Proc. 39th IEEE\/ACM Int. Conf. Automat. Softw. Eng.","author":"Luo","year":"2024"},{"issue":"240","key":"ref75","first-page":"1","article-title":"PaLM: Scaling language modeling with pathways","volume":"24","author":"Chowdhery","year":"2023","journal-title":"J. Mach. Learn. Res."},{"year":"2023","author":"Liu","article-title":"In-context vectors: Making in context learning more effective and controllable through latent space steering","key":"ref76"},{"year":"2022","author":"Wei","article-title":"Emergent abilities of large language models","key":"ref77"},{"key":"ref78","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":"ref79","article-title":"Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Lu","year":"2023"},{"year":"2023","author":"Scarlatos","article-title":"RetICL: Sequential retrieval of in-context examples with reinforcement learning","key":"ref80"},{"doi-asserted-by":"publisher","key":"ref81","DOI":"10.18653\/v1\/2022.findings-emnlp.384"},{"key":"ref82","first-page":"35151","article-title":"Transformers learn in-context by gradient descent","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Von Oswald","year":"2023"},{"key":"ref83","first-page":"30583","article-title":"What can transformers learn in-context? A case study of simple function classes","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Garg","year":"2022"},{"year":"2024","author":"Liu","article-title":"Let\u2019s learn step by step: Enhancing in-context learning ability with curriculum learning","key":"ref84"},{"doi-asserted-by":"publisher","key":"ref85","DOI":"10.18653\/v1\/2023.acl-long.256"},{"doi-asserted-by":"publisher","key":"ref86","DOI":"10.18653\/v1\/2023.emnlp-main.758"}],"container-title":["IEEE Transactions on Software Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/32\/11251265\/11150549.pdf?arnumber=11150549","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T06:15:46Z","timestamp":1763532946000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11150549\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":86,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tse.2025.3605768","relation":{},"ISSN":["0098-5589","1939-3520","2326-3881"],"issn-type":[{"type":"print","value":"0098-5589"},{"type":"electronic","value":"1939-3520"},{"type":"electronic","value":"2326-3881"}],"subject":[],"published":{"date-parts":[[2025,11]]}}}