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Large Language Models (LLMs) have shown high capabilities in several software development-related tasks such as program repair, documentation, code refactoring, debugging, and testing.
Chen Z, Kommrusch S, Tufano M, Pouchet LN, Poshyvanyk D, Monperrus M (2019) Sequencer: Sequence-to-sequence learning for end-to-end program repair. IEEE Transactions on Software Engineering 47(9):1943–1959
1959
Earlier work this paper cites.
Liu H, Tam D, Muqeeth M, Mohta J, Huang T, Bansal M, Raffel CA (2022) Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning. Advances in Neural Information Processing Systems 35:1950–1965
1965
Earlier work this paper cites.
Cliff N (1993) Dominance statistics: Ordinal analyses to answer ordinal questions. Psychological Bulletin 114(3):494–509
1993
Earlier work this paper cites.
Papineni K, Roukos S, Ward T, Zhu WJ (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318
2002
Earlier work this paper cites.
Jobstmann B, Griesmayer A, Bloem R (2005) Program repair as a game. In: Computer Aided Verification: 17th International Conference, CAV 2005, Edinburgh, Scotland, UK, July 6-10, 2005. Proceedings 17, Springer, pp 226–238
2005
Earlier work this paper cites.
Weimer W, Nguyen T, Le Goues C, Forrest S (2009) Automatically finding patches using genetic programming. In: 2009 IEEE 31st International Conference on Software Engineering, IEEE, pp 364–374
2009
Earlier work this paper cites.
Torrey L, Shavlik J (2010) Transfer learning. In: Handbook of research on machine learning applications and trends: algorithms, methods, and techniques, IGI global, pp 242–264
2010
Earlier work this paper cites.
Wei Y, Pei Y, Furia CA, Silva LS, Buchholz S, Meyer B, Zeller A (2010) Automated fixing of programs with contracts. In: Proceedings of the 19th international symposium on Software testing and analysis, pp 61–72
2010
Earlier work this paper cites.
Weimer W, Forrest S, Le Goues C, Nguyen T (2010) Automatic program repair with evolutionary computation. Communications of the ACM 53(5):109–116
2010
Earlier work this paper cites.
Le Goues C, Dewey-Vogt M, Forrest S, Weimer W (2012) A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each. In: 2012 34th international conference on software engineering (ICSE), IEEE, pp 3–13
2012
Earlier work this paper cites.
Nguyen HDT, Qi D, Roychoudhury A, Chandra S (2013) Semfix: Program repair via semantic analysis. In: 2013 35th International Conference on Software Engineering (ICSE), IEEE, pp 772–781
2013
Earlier work this paper cites.
Kim TK (2015) T test as a parametric statistic. Korean journal of anesthesiology 68(6):540–546
2015
Earlier work this paper cites.
Liu X, Gao J, He X, Deng L, Duh K, Wang Yy (2015) Representation learning using multi-task deep neural networks for semantic classification and information retrieval. In: Mihalcea R, Chai J, Sarkar A (eds) Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Denver, Colorado, pp 912–921, DOI 10.3115/v1/N15-1092
2015
Earlier work this paper cites.
Xuan J, Martinez M, Demarco F, Clement M, Marcote SL, Durieux T, Le Berre D, Monperrus M (2016) Nopol: Automatic repair of conditional statement bugs in java programs. IEEE Transactions on Software Engineering 43(1):34–55
2016
Earlier work this paper cites.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Howard J, Ruder S (2018) Universal language model fine-tuning for text classification. In: Gurevych I, Miyao Y (eds) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Melbourne, Australia, pp 328–339, DOI 10.18653/v1/P18-1031
2018
Earlier work this paper cites.
Zhang Y, Yang Q (2018) An overview of multi-task learning. National Science Review 5(1):30–43
2018
Earlier work this paper cites.
Mesbah A, Rice A, Johnston E, Glorioso N, Aftandilian E (2019) Deepdelta: learning to repair compilation errors. In: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp 925–936
2019
Earlier work this paper cites.
Hu X, Li G, Xia X, Lo D, Jin Z (2020) Deep code comment generation with hybrid lexical and syntactical information. Empirical Software Engineering 25:2179–2217
2020
Earlier work this paper cites.
Liu S, Gao C, Chen S, Nie LY, Liu Y (2020) Atom: Commit message generation based on abstract syntax tree and hybrid ranking. IEEE Transactions on Software Engineering 48(5):1800–1817
2020
Earlier work this paper cites.
Lutellier T, Pham HV, Pang L, Li Y, Wei M, Tan L (2020) Coconut: combining context-aware neural translation models using ensemble for program repair. In: Proceedings of the 29th ACM SIGSOFT international symposium on software testing and analysis, pp 101–114
2020
Earlier work this paper cites.
Motwani M, Brun Y (2020) Automatically repairing programs using both tests and bug reports. arXiv preprint arXiv:201108340
2020
Earlier work this paper cites.
Pandey SK, Mishra RB, Tripathi AK (2020) Bpdet: An effective software bug prediction model using deep representation and ensemble learning techniques. Expert Systems with Applications 144:113085
2020
Earlier work this paper cites.
Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research 21(140):1–67
2020
Earlier work this paper cites.
Ren S, Guo D, Lu S, Zhou L, Liu S, Tang D, Sundaresan N, Zhou M, Blanco A, Ma S (2020) Codebleu: a method for automatic evaluation of code synthesis. arXiv preprint arXiv:200910297
2020
Earlier work this paper cites.
Chen M, Tworek J, Jun H, Yuan Q, Pinto HPdO, Kaplan J, Edwards H, Burda Y, Joseph N, Brockman G, et al. (2021) Evaluating large language models trained on code. arXiv preprint arXiv:210703374
2021
Earlier work this paper cites.
Liu K, Li L, Koyuncu A, Kim D, Liu Z, Klein J, Bissyandé TF (2021) A critical review on the evaluation of automated program repair systems. Journal of Systems and Software 171:110817
2021
Cited alongside, same era.
Pfeiffer J, Kamath A, Rücklé A, Cho K, Gurevych I (2021) AdapterFusion: Non-destructive task composition for transfer learning. In: Merlo P, Tiedemann J, Tsarfaty R (eds) Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Association for Computational Linguistics, Online, pp 487–503, DOI 10.18653/v1/2021.eacl-main.39
2021
Cited alongside, same era.
Wong CP, Santiesteban P, Kästner C, Le Goues C (2021) Varfix: balancing edit expressiveness and search effectiveness in automated program repair. In: Proceedings of the 29th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering, pp 354–366
2021
Cited alongside, same era.
Xia CS, Wei Y, Zhang L (2023) Automated program repair in the era of large pre-trained language models. In: Proceedings of the 45th International Conference on Software Engineering, IEEE Press, ICSE ’23, p 1482–1494, DOI 10.1109/ICSE48619.2023.00129
2023
Later among the works it cites.
Yang E, Wang Z, Shen L, Liu S, Guo G, Wang X, Tao D (2023) Adamerging: Adaptive model merging for multi-task learning. arXiv preprint arXiv:231002575
2023
Later among the works it cites.
Yu L, Yu B, Yu H, Huang F, Li Y (2023) Language models are super mario: Absorbing abilities from homologous models as a free lunch. arXiv preprint arXiv:231103099
2023
Later among the works it cites.
Zhang J, Liu J, He J, et al. (2023) Composing parameter-efficient modules with arithmetic operation. Advances in Neural Information Processing Systems 36:12589–12610
2023
Later among the works it cites.
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Ainsworth S, Hayase J, Srinivasa S (2022) Git re-basin: Merging models modulo permutation symmetries. In: The Eleventh International Conference on Learning Representations
2022
Cited alongside, same era.
Bavarian M, Jun H, Tezak N, Schulman J, McLeavey C, Tworek J, Chen M (2022) Efficient training of language models to fill in the middle. arXiv preprint arXiv:220714255
2022
Cited alongside, same era.
Fu M, Tantithamthavorn C, Le T, Nguyen V, Phung D (2022) Vulrepair: a t5-based automated software vulnerability repair. In: Proceedings of the 30th ACM joint european software engineering conference and symposium on the foundations of software engineering, pp 935–947
2022
Cited alongside, same era.
Hu EJ, Shen Y, Wallis P, Allen-Zhu Z, Li Y, Wang S, Wang L, Chen W (2022) LoRA: Low-rank adaptation of large language models. In: International Conference on Learning Representations, URL https://openreview.net/forum?id=nZeVKeeFYf9
2022
Cited alongside, same era.
Matena MS, Raffel CA (2022) Merging models with fisher-weighted averaging. Advances in Neural Information Processing Systems 35:17703–17716
2022
Cited alongside, same era.
Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C, Mishkin P, Zhang C, Agarwal S, Slama K, Ray A, et al. (2022) Training language models to follow instructions with human feedback. Advances in neural information processing systems 35:27730–27744
2022
Cited alongside, same era.
Sung YL, Cho J, Bansal M (2022) Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 5227–5237
2022
Cited alongside, same era.
Wang Y, Agarwal S, Mukherjee S, Liu X, Gao J, Awadallah AH, Gao J (2022) AdaMix: Mixture-of-adaptations for parameter-efficient model tuning. In: Goldberg Y, Kozareva Z, Zhang Y (eds) Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, pp 5744–5760, DOI 10.18653/v1/2022.emnlp-main.388
2022
Cited alongside, same era.
Wortsman M, Ilharco G, Gadre SY, Roelofs R, Gontijo-Lopes R, Morcos AS, Namkoong H, Farhadi A, Carmon Y, Kornblith S, et al. (2022) Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. In: International conference on machine learning, PMLR, pp 23965–23998
2022
Cited alongside, same era.
Chen H, Tao R, Zhang H, Wang Y, Li X, Ye W, Wang J, Hu G, Savvides M (2024) Conv-adapter: Exploring parameter efficient transfer learning for convnets. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 1551–1561
2024
Closest in time.
Goddard C, Siriwardhana S, Ehghaghi M, Meyers L, Karpukhin V, Benedict B, McQuade M, Solawetz J (2024) Arcee’s mergekit: A toolkit for merging large language models. arXiv preprint arXiv:240313257
2024
Closest in time.
Huang C, Ye P, Chen T, He T, Yue X, Ouyang W (2024) Emr-merging: Tuning-free high-performance model merging. Advances in Neural Information Processing Systems 37:122741–122769
2024
Closest in time.
Koyuncu A, Liu K, Bissyandé TF, Kim D, Klein J, Monperrus M, Le Traon Y (2020) Fixminer: Mining relevant fix patterns for automated program repair. Empirical Software Engineering 25:1980–2024
2024
Closest in time.
Liu S, Keung J, Yang Z, Liu F, Zhou Q, Liao Y (2024) Delving into parameter-efficient fine-tuning in code change learning: An empirical study. arXiv preprint arXiv:240206247
2024
Closest in time.
Lozhkov A, Li R, Allal LB, Cassano F, Lamy-Poirier J, Tazi N, Tang A, Pykhtar D, Liu J, Wei Y, et al. (2024) Starcoder 2 and the stack v2: The next generation. arXiv preprint arXiv:240219173
2024
Closest in time.
Mishra M, Stallone M, Zhang G, Shen Y, Prasad A, Soria AM, Merler M, Selvam P, Surendran S, Singh S, et al. (2024) Granite code models: A family of open foundation models for code intelligence. arXiv preprint arXiv:240504324
2024
Closest in time.
Nguyen T, Le T (2024) Generalizability of mixture of domain-specific adapters from the lens of signed weight directions and its application to effective model pruning. arXiv preprint arXiv:240210639
2024
Closest in time.
Prabhakar A, Li Y, Narasimhan K, Kakade S, Malach E, Jelassi S (2024) Lora soups: Merging loras for practical skill composition tasks. arXiv preprint arXiv:241013025
2024
Closest in time.
Shi J, Yang Z, Lo D (2024) Efficient and green large language models for software engineering: Vision and the road ahead. ACM Transactions on Software Engineering and Methodology
2024
Closest in time.
Stoica G, Bolya D, Bjorner JB, Ramesh P, Hearn T, Hoffman J (2024) Zipit! merging models from different tasks without training. In: The Twelfth International Conference on Learning Representations, URL https://openreview.net/forum?id=LEYUkvdUhq
2024
Closest in time.
Wu X, Huang S, Wei F (2024) Mixture of lora experts. arXiv preprint arXiv:240413628
2024
Closest in time.
Xin Y, Luo S, Liu X, Zhou H, Cheng X, Lee CE, Du J, Wang H, Chen M, Liu T, et al. (2024) V-petl bench: A unified visual parameter-efficient transfer learning benchmark. Advances in Neural Information Processing Systems 37:80522–80535
2024
Closest in time.
Yadav P, Tam D, Choshen L, Raffel CA, Bansal M (2024) Ties-merging: Resolving interference when merging models. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Yang E, Shen L, Guo G, Wang X, Cao X, Zhang J, Tao D (2024) Model merging in llms, mllms, and beyond: Methods, theories, applications and opportunities. arXiv preprint arXiv:240807666
2024
Closest in time.
Zhao Y, Zhang W, Wang H, Kawaguchi K, Bing L (2024) Adamergex: Cross-lingual transfer with large language models via adaptive adapter merging. arXiv preprint arXiv:240218913
2024
Closest in time.
Zhuo TY, Zebaze A, Suppattarachai N, von Werra L, de Vries H, Liu Q, Muennighoff N (2024) Astraios: Parameter-efficient instruction tuning code large language models. arXiv preprint arXiv:240100788
2024
Closest in time.
Akiba T, Shing M, Tang Y, Sun Q, Ha D (2025) Evolutionary optimization of model merging recipes. Nature Machine Intelligence pp 1–10
2025
Closest in time.
Haque MZ, Afrin S, Mastropaolo A (2025) A systematic literature review of parameter-efficient fine-tuning for large code models. arXiv preprint arXiv:250421569
2025
Closest in time.
Männistö J, Attieh J, Tiedemann J (2025) A comparative study of PEFT methods for python code generation. In: Johansson R, Stymne S (eds) Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025), University of Tartu Library, Tallinn, Estonia, pp 390–396, URL https://aclanthology.org/2025.nodalida-1.42/
2025
Closest in time.