Built on

F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics Bulletin , vol. 1, no. 6, pp. 80–83, 1945
1945
Earlier work this paper cites.
S. Holm, “A simple sequentially rejective multiple test procedure,” Scandinavian journal of statistics , pp. 65–70, 1979
1979
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting of the Association for Computational Linguistics , 2002, pp. 311–318
2002
Earlier work this paper cites.
C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” in Text summarization branches out , 2004, pp. 74–81
2004
Earlier work this paper cites.
K. Krippendorff, “Reliability in content analysis: Some common misconceptions and recommendations,” Human communication research , vol. 30, no. 3, pp. 411–433, 2004
2004
Earlier work this paper cites.
S. Banerjee and A. Lavie, “Meteor: An automatic metric for mt evaluation with improved correlation with human judgments,” in Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization , 2005, pp. 65–72
2005
Earlier work this paper cites.
R. J. Grissom and J. J. Kim, Effect sizes for research: A broad practical approach , 2nd ed. Lawrence Earlbaum Associates, 2005
2005
Earlier work this paper cites.
M. Popović, “chrf: character n-gram f-score for automatic mt evaluation,” in Proceedings of the tenth workshop on statistical machine translation , 2015, pp. 392–395
2015
Earlier work this paper cites.
J. Devlin, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018
Original
2018
Earlier work this paper cites.
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in International conference on machine learning . PMLR, 2019, pp. 2790–2799
2019
Earlier work this paper cites.
T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, and Y. Artzi, “Bertscore: Evaluating text generation with bert,” arXiv preprint arXiv:1904.09675 , 2019
Original
2019
Earlier work this paper cites.
J. Zhang, X. Wang, H. Zhang, H. Sun, and X. Liu, “Retrieval-based neural source code summarization,” in Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering , 2020, pp. 1385–1397
2020
Earlier work this paper cites.
W. U. Ahmad, S. Chakraborty, B. Ray, and K.-W. Chang, “A transformer-based approach for source code summarization,” arXiv preprint arXiv:2005.00653 , 2020
Original
2020
Earlier work this paper cites.
D. Gros, H. Sezhiyan, P. Devanbu, and Z. Yu, “Code to comment” translation” data, metrics, baselining & evaluation,” in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering , 2020, pp. 746–757
2020
Earlier work this paper cites.
T. B. Brown, “Language models are few-shot learners,” arXiv preprint arXiv:2005.14165 , 2020
Original
2020
Earlier work this paper cites.
A. LeClair, S. Haque, L. Wu, and C. McMillan, “Improved code summarization via a graph neural network,” in Proceedings of the 28th international conference on program comprehension , 2020, pp. 184–195
2020
Earlier work this paper cites.
A. Mastropaolo, S. Scalabrino, N. Cooper, D. N. Palacio, D. Poshyvanyk, R. Oliveto, and G. Bavota, “Studying the usage of text-to-text transfer transformer to support code-related tasks,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering . IEEE, 2021, pp. 336–347
2021
Earlier work this paper cites.
Y. Wang, W. Wang, S. Joty, and S. C. Hoi, “Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation,” arXiv preprint arXiv:2109.00859 , 2021
Original
2021
Earlier work this paper cites.
B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,” arXiv preprint arXiv:2104.08691 , 2021
Original
2021
Earlier work this paper cites.
X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” arXiv preprint arXiv:2101.00190 , 2021
Original
2021
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” arXiv preprint arXiv:2106.09685 , 2021
Original
2021
Earlier work this paper cites.
W. U. Ahmad, S. Chakraborty, B. Ray, and K.-W. Chang, “Unified pre-training for program understanding and generation,” arXiv preprint arXiv:2103.06333 , 2021
Original
2021
Earlier work this paper cites.
S. Lu, D. Guo, S. Ren, J. Huang, A. Svyatkovskiy, A. Blanco, C. Clement, D. Drain, D. Jiang, D. Tang et al. , “Codexglue: A machine learning benchmark dataset for code understanding and generation,” arXiv preprint arXiv:2102.04664 , 2021
Original
2021
Earlier work this paper cites.
T. Dettmers, M. Lewis, S. Shleifer, and L. Zettlemoyer, “8-bit optimizers via block-wise quantization,” arXiv preprint arXiv:2110.02861 , 2021
Original
2021
Earlier work this paper cites.
A. Mastropaolo, E. Aghajani, L. Pascarella, and G. Bavota, “An empirical study on code comment completion,” in 2021 IEEE International Conference on Software Maintenance and Evolution . IEEE, 2021, pp. 159–170
2021
Earlier work this paper cites.
D. Roy, S. Fakhoury, and V. Arnaoudova, “Reassessing automatic evaluation metrics for code summarization tasks,” in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2021, pp. 1105–1116
2021
Earlier work this paper cites.
H. Liu, D. Tam, M. Muqeeth, J. Mohta, T. Huang, M. Bansal, and C. A. Raffel, “Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,” Advances in Neural Information Processing Systems , vol. 35, pp. 1950–1965, 2022
2022
Earlier work this paper cites.
T. Ahmed and P. Devanbu, “Few-shot training llms for project-specific code-summarization,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–5
2022
Earlier work this paper cites.
C. Wang, Y. Yang, C. Gao, Y. Peng, H. Zhang, and M. R. Lyu, “No more fine-tuning? an experimental evaluation of prompt tuning in code intelligence,” in Proceedings of the 30th ACM joint European software engineering conference and symposium on the foundations of software engineering , 2022, pp. 382–394
2022
Earlier work this paper cites.

Similar

S. Ayupov and N. Chirkova, “Parameter-efficient finetuning of transformers for source code,” arXiv preprint arXiv:2212.05901 , 2022
Original
2022
Cited alongside, same era.
E. Shi, Y. Wang, L. Du, J. Chen, S. Han, H. Zhang, D. Zhang, and H. Sun, “On the evaluation of neural code summarization,” in Proceedings of the 44th international conference on software engineering , 2022, pp. 1597–1608
2022
Cited alongside, same era.
A. Mastropaolo, L. Pascarella, and G. Bavota, “Using deep learning to generate complete log statements,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 2279–2290
2022
Cited alongside, same era.
F. Chen, F. H. Fard, D. Lo, and T. Bryksin, “On the transferability of pre-trained language models for low-resource programming languages,” in Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension , 2022, pp. 401–412
2022
Cited alongside, same era.

Then

X. Zhu, J. Li, Y. Liu, C. Ma, and W. Wang, “A survey on model compression for large language models,” arXiv preprint arXiv:2308.07633 , 2023
Original
2023
Later among the works it cites.
L. Tunstall, N. Lambert, N. Rajani, E. Beeching, T. Le Scao, L. von Werra, S. Han, P. Schmid, and A. Rush, “Creating a coding assistant with starcoder,” Hugging Face Blog , 2023, https://huggingface.co/blog/starchat-alpha
2023
Later among the works it cites.
A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. d. l. Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier et al. , “Mistral 7b,” arXiv preprint arXiv:2310.06825 , 2023
Original
2023
Later among the works it cites.
Z. Yuan, J. Liu, Q. Zi, M. Liu, X. Peng, and Y. Lou, “Evaluating instruction-tuned large language models on code comprehension and generation,” arXiv preprint arXiv:2308.01240 , 2023
Original
2023
Later among the works it cites.

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2025

Resource-Efficient & Effective Code Summarization

Afrin, Saima, Call, Joseph, Nguyen, Khai-Nguyen et al.

Understand

Code Language Models (CLMs) have demonstrated high effectiveness in automating software engineering tasks such as bug fixing, code generation, and code documentation.

This progress has been driven by the scaling of large models, ranging from millions to trillions of parameters (e.g., GPT-4).
However, as models grow in scale, sustainability concerns emerge, as they are extremely resource-intensive, highlighting the need for efficient, environmentally conscious solutions.
GreenAI techniques, such as QLoRA (Quantized Low-Rank Adaptation), offer a promising path for dealing with large models' sustainability as they enable resource-efficient model fine-tuning.

Built on

F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics Bulletin , vol. 1, no. 6, pp. 80–83, 1945
1945
Earlier work this paper cites.
S. Holm, “A simple sequentially rejective multiple test procedure,” Scandinavian journal of statistics , pp. 65–70, 1979
1979
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting of the Association for Computational Linguistics , 2002, pp. 311–318
2002
Earlier work this paper cites.
C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” in Text summarization branches out , 2004, pp. 74–81
2004
Earlier work this paper cites.
K. Krippendorff, “Reliability in content analysis: Some common misconceptions and recommendations,” Human communication research , vol. 30, no. 3, pp. 411–433, 2004
2004
Earlier work this paper cites.
S. Banerjee and A. Lavie, “Meteor: An automatic metric for mt evaluation with improved correlation with human judgments,” in Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization , 2005, pp. 65–72
2005
Earlier work this paper cites.
R. J. Grissom and J. J. Kim, Effect sizes for research: A broad practical approach , 2nd ed. Lawrence Earlbaum Associates, 2005
2005
Earlier work this paper cites.
M. Popović, “chrf: character n-gram f-score for automatic mt evaluation,” in Proceedings of the tenth workshop on statistical machine translation , 2015, pp. 392–395
2015
Earlier work this paper cites.
J. Devlin, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018
Original
2018
Earlier work this paper cites.
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in International conference on machine learning . PMLR, 2019, pp. 2790–2799
2019
Earlier work this paper cites.
T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, and Y. Artzi, “Bertscore: Evaluating text generation with bert,” arXiv preprint arXiv:1904.09675 , 2019
Original
2019
Earlier work this paper cites.
J. Zhang, X. Wang, H. Zhang, H. Sun, and X. Liu, “Retrieval-based neural source code summarization,” in Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering , 2020, pp. 1385–1397
2020
Earlier work this paper cites.
W. U. Ahmad, S. Chakraborty, B. Ray, and K.-W. Chang, “A transformer-based approach for source code summarization,” arXiv preprint arXiv:2005.00653 , 2020
Original
2020
Earlier work this paper cites.
D. Gros, H. Sezhiyan, P. Devanbu, and Z. Yu, “Code to comment” translation” data, metrics, baselining & evaluation,” in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering , 2020, pp. 746–757
2020
Earlier work this paper cites.
T. B. Brown, “Language models are few-shot learners,” arXiv preprint arXiv:2005.14165 , 2020
Original
2020
Earlier work this paper cites.
A. LeClair, S. Haque, L. Wu, and C. McMillan, “Improved code summarization via a graph neural network,” in Proceedings of the 28th international conference on program comprehension , 2020, pp. 184–195
2020
Earlier work this paper cites.
A. Mastropaolo, S. Scalabrino, N. Cooper, D. N. Palacio, D. Poshyvanyk, R. Oliveto, and G. Bavota, “Studying the usage of text-to-text transfer transformer to support code-related tasks,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering . IEEE, 2021, pp. 336–347
2021
Earlier work this paper cites.
Y. Wang, W. Wang, S. Joty, and S. C. Hoi, “Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation,” arXiv preprint arXiv:2109.00859 , 2021
Original
2021
Earlier work this paper cites.
B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,” arXiv preprint arXiv:2104.08691 , 2021
Original
2021
Earlier work this paper cites.
X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” arXiv preprint arXiv:2101.00190 , 2021
Original
2021
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” arXiv preprint arXiv:2106.09685 , 2021
Original
2021
Earlier work this paper cites.
W. U. Ahmad, S. Chakraborty, B. Ray, and K.-W. Chang, “Unified pre-training for program understanding and generation,” arXiv preprint arXiv:2103.06333 , 2021
Original
2021
Earlier work this paper cites.
S. Lu, D. Guo, S. Ren, J. Huang, A. Svyatkovskiy, A. Blanco, C. Clement, D. Drain, D. Jiang, D. Tang et al. , “Codexglue: A machine learning benchmark dataset for code understanding and generation,” arXiv preprint arXiv:2102.04664 , 2021
Original
2021
Earlier work this paper cites.
T. Dettmers, M. Lewis, S. Shleifer, and L. Zettlemoyer, “8-bit optimizers via block-wise quantization,” arXiv preprint arXiv:2110.02861 , 2021
Original
2021
Earlier work this paper cites.
A. Mastropaolo, E. Aghajani, L. Pascarella, and G. Bavota, “An empirical study on code comment completion,” in 2021 IEEE International Conference on Software Maintenance and Evolution . IEEE, 2021, pp. 159–170
2021
Earlier work this paper cites.
D. Roy, S. Fakhoury, and V. Arnaoudova, “Reassessing automatic evaluation metrics for code summarization tasks,” in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2021, pp. 1105–1116
2021
Earlier work this paper cites.
H. Liu, D. Tam, M. Muqeeth, J. Mohta, T. Huang, M. Bansal, and C. A. Raffel, “Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,” Advances in Neural Information Processing Systems , vol. 35, pp. 1950–1965, 2022
2022
Earlier work this paper cites.
T. Ahmed and P. Devanbu, “Few-shot training llms for project-specific code-summarization,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–5
2022
Earlier work this paper cites.
C. Wang, Y. Yang, C. Gao, Y. Peng, H. Zhang, and M. R. Lyu, “No more fine-tuning? an experimental evaluation of prompt tuning in code intelligence,” in Proceedings of the 30th ACM joint European software engineering conference and symposium on the foundations of software engineering , 2022, pp. 382–394
2022
Earlier work this paper cites.

Similar

S. Ayupov and N. Chirkova, “Parameter-efficient finetuning of transformers for source code,” arXiv preprint arXiv:2212.05901 , 2022
Original
2022
Cited alongside, same era.
E. Shi, Y. Wang, L. Du, J. Chen, S. Han, H. Zhang, D. Zhang, and H. Sun, “On the evaluation of neural code summarization,” in Proceedings of the 44th international conference on software engineering , 2022, pp. 1597–1608
2022
Cited alongside, same era.
A. Mastropaolo, L. Pascarella, and G. Bavota, “Using deep learning to generate complete log statements,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 2279–2290
2022
Cited alongside, same era.
F. Chen, F. H. Fard, D. Lo, and T. Bryksin, “On the transferability of pre-trained language models for low-resource programming languages,” in Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension , 2022, pp. 401–412
2022
Cited alongside, same era.

Then

X. Zhu, J. Li, Y. Liu, C. Ma, and W. Wang, “A survey on model compression for large language models,” arXiv preprint arXiv:2308.07633 , 2023
Original
2023
Later among the works it cites.
L. Tunstall, N. Lambert, N. Rajani, E. Beeching, T. Le Scao, L. von Werra, S. Han, P. Schmid, and A. Rush, “Creating a coding assistant with starcoder,” Hugging Face Blog , 2023, https://huggingface.co/blog/starchat-alpha
2023
Later among the works it cites.
A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. d. l. Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier et al. , “Mistral 7b,” arXiv preprint arXiv:2310.06825 , 2023
Original
2023
Later among the works it cites.
Z. Yuan, J. Liu, Q. Zi, M. Liu, X. Peng, and Y. Lou, “Evaluating instruction-tuned large language models on code comprehension and generation,” arXiv preprint arXiv:2308.01240 , 2023
Original
2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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J. Gu, P. Salza, and H. C. Gall, “Assemble foundation models for automatic code summarization,” in 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering . IEEE, 2022, pp. 935–946

2022

Cited alongside, same era.

A. Gholami, S. Kim, Z. Dong, Z. Yao, M. W. Mahoney, and K. Keutzer, “A survey of quantization methods for efficient neural network inference,” in Low-Power Computer Vision . Chapman and Hall/CRC, 2022, pp. 291–326

2022

Cited alongside, same era.

D. Fried, A. Aghajanyan, J. Lin, S. Wang, E. Wallace, F. Shi, R. Zhong, W.-t. Yih, L. Zettlemoyer, and M. Lewis, “Incoder: A generative model for code infilling and synthesis,” arXiv preprint arXiv:2204.05999 , 2022