Fetching the paper…
Reading the bibliography…
A code summary is a brief natural language description of source code.
Israel GD (1992) Determining sample size
1992
Earlier work this paper cites.
Donker D, Hasman A, Van Geijn H (1993) Interpretation of low kappa values. International journal of bio-medical computing 33(1):55–64
1993
Earlier work this paper cites.
Forward A, Lethbridge TC (2002) The relevance of software documentation, tools and technologies: A survey. In: Proceedings of the 2002 ACM Symposium on Document Engineering. Association for Computing Machinery, New York, NY, USA, DocEng ’02, p 26–33, 10.1145/585058.585065
2002
Earlier work this paper cites.
Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting on association for computational linguistics, Association for Computational Linguistics, pp 311–318, 10.3115/1073083.1073135
2002
Earlier work this paper cites.
Banerjee S, Lavie A (2005) 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, pp 65–72, URL https://aclanthology.org/W05-0909
2005
Earlier work this paper cites.
Haiduc S, Aponte J, Moreno L, et al (2010) On the use of automated text summarization techniques for summarizing source code. In: 2010 17th Working Conference on Reverse Engineering, IEEE, pp 35–44, 10.1109/WCRE.2010.13
2010
Earlier work this paper cites.
Sridhara G, Hill E, Muppaneni D, et al (2010) Towards automatically generating summary comments for java methods. In: Proceedings of the IEEE/ACM international conference on Automated software engineering, ACM, pp 43–52, 10.1145/1858996.1859006
2010
Earlier work this paper cites.
Dell N, Vaidyanathan V, Medhi I, et al (2012) ” yours is better!” participant response bias in hci. In: Proceedings of the sigchi conference on human factors in computing systems, pp 1321–1330, 10.1145/2207676.2208589
2012
Earlier work this paper cites.
McBurney PW, Liu C, McMillan C (2016) Automated feature discovery via sentence selection and source code summarization. Journal of Software: Evolution and Process 28(2):120–145. 10.1002/smr.1768
2016
Earlier work this paper cites.
Sievertsen HH, Gino F, Piovesan M (2016) Cognitive fatigue influences students’ performance on standardized tests. Proceedings of the National Academy of Sciences 113(10):2621–2624. 10.1073/pnas.1516947113
2016
Earlier work this paper cites.
Zagoruyko S, Komodakis N (2016) Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. In: International Conference on Learning Representations
2016
Earlier work this paper cites.
Fowkes J, Chanthirasegaran P, Ranca R, et al (2017) Autofolding for source code summarization. IEEE Transactions on Software Engineering 43(12):1095–1109. 10.1109/TSE.2017.2664836
2017
Earlier work this paper cites.
Jiang S, Armaly A, McMillan C (2017) Automatically generating commit messages from diffs using neural machine translation. In: Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering. IEEE Press, ASE ’17, p 135–146
2017
Earlier work this paper cites.
Novikova J, Dušek O, Cercas Curry A, et al (2017) Why we need new evaluation metrics for NLG. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Copenhagen, Denmark, pp 2241–2252, 10.18653/v1/D17-1238 , URL https://aclanthology.org/D17-1238
2017
Earlier work this paper cites.
Robillard MP, Marcus A, Treude C, et al (2017) On-demand developer documentation. In: 2017 IEEE International conference on software maintenance and evolution (ICSME), IEEE, pp 479–483, 10.1109/ICSME.2017.17
2017
Earlier work this paper cites.
Rodeghero P, Jiang S, Armaly A, et al (2017) Detecting user story information in developer-client conversations to generate extractive summaries. In: 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE), pp 49–59, 10.1109/ICSE.2017.13
2017
Earlier work this paper cites.
Liang Y, Zhu KQ (2018) Automatic generation of text descriptive comments for code blocks. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI Press, AAAI’18/IAAI’18/EAAI’18
2018
Earlier work this paper cites.
Wan Y, Zhao Z, Yang M, et al (2018) Improving automatic source code summarization via deep reinforcement learning. In: Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering. Association for Computing Machinery, New York, NY, USA, ASE ’18, p 397–407, 10.1145/3238147.3238206 , URL https://doi.org/10.1145/3238147.3238206
2018
Earlier work this paper cites.
Aghajani E, Nagy C, Vega-Márquez OL, et al (2019) Software documentation issues unveiled. In: 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), IEEE, pp 1199–1210
2019
Earlier work this paper cites.
Delgado R, Tibau XA (2019) Why cohen’s kappa should be avoided as performance measure in classification. PloS one 14(9):e0222916
2019
Earlier work this paper cites.
Gao S, Chen C, Xing Z, et al (2019) A neural model for method name generation from functional description. In: 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE, pp 414–421, 10.1109/SANER.2019.8667994
2019
Earlier work this paper cites.
LeClair A, McMillan C (2019) Recommendations for datasets for source code summarization. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp 3931–3937
2019
Earlier work this paper cites.
LeClair A, Jiang S, McMillan C (2019) A neural model for generating natural language summaries of program subroutines. In: Proceedings of the 41st International Conference on Software Engineering, IEEE Press, pp 795–806, 10.1109/ICSE.2019.00087
2019
Cited alongside, same era.
Lu Y, Zhao Z, Li G, et al (2019) Learning to generate comments for api-based code snippets. In: Li Z, Jiang H, Li G, et al (eds) Software Engineering and Methodology for Emerging Domains. Springer Singapore, Singapore, pp 3–14
2019
Cited alongside, same era.
Nie P, Rai R, Li JJ, et al (2019) A framework for writing trigger-action todo comments in executable format. In: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. Association for Computing Machinery, New York, NY, USA, ESEC/FSE 2019, p 385–396, 10.1145/3338906.3338965
2019
Cited alongside, same era.
Haque S, Eberhart Z, Bansal A, et al (2022) Semantic similarity metrics for evaluating source code summarization. In: Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension, pp 36–47, 10.1145/3524610.3527909
2022
Later among the works it cites.
Li Z, Wu Y, Peng B, et al (2023c) Setransformer: A transformer-based code semantic parser for code comment generation. IEEE Transactions on Reliability 72(1):258–273. 10.1109/TR.2022.3154773
2022
Later among the works it cites.
OpenAI (2022) Chatgpt. URL https://openai.com/blog/chatgpt
2022
Later among the works it cites.
Shi L, Mu F, Chen X, et al (2022) Are we building on the rock? on the importance of data preprocessing for code summarization. In: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. Association for Computing Machinery, ESEC/FSE 2022, p 107–119
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ahmad W, Chakraborty S, Ray B, et al (2020) A transformer-based approach for source code summarization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 4998–5007, 10.18653/v1/2020.acl-main.449 , URL https://aclanthology.org/2020.acl-main.449
2020
Cited alongside, same era.
Brown T, Mann B, Ryder N, et al (2020) Language models are few-shot learners. In: Larochelle H, Ranzato M, Hadsell R, et al (eds) Advances in Neural Information Processing Systems, vol 33. Curran Associates, Inc., pp 1877–1901, URL https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
2020
Cited alongside, same era.
Haldar R, Wu L, Xiong J, et al (2020) A multi-perspective architecture for semantic code search. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8563–8568, 10.18653/v1/2020.acl-main.758 , URL https://aclanthology.org/2020.acl-main.758
2020
Cited alongside, same era.
Haque S, LeClair A, Wu L, et al (2020) Improved automatic summarization of subroutines via attention to file context. International Conference on Mining Software Repositories 10.1145/3379597.3387449
2020
Cited alongside, same era.
Bansal A, Eberhart Z, Wu L, et al (2021a) A neural question answering system for basic questions about subroutines. In: 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pp 60–71, 10.1109/SANER50967.2021.00015
2021
Cited alongside, same era.
Bansal A, Haque S, McMillan C (2021b) Project-level encoding for neural source code summarization of subroutines. In: 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC), IEEE, pp 253–264
2021
Cited alongside, same era.
Bender EM, Gebru T, McMillan-Major A, et al (2021) On the dangers of stochastic parrots: Can language models be too big? In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, New York, NY, USA, FAccT ’21, p 610–623, 10.1145/3442188.3445922
2021
Cited alongside, same era.
Danilova A, Naiakshina A, Horstmann S, et al (2021) Do you really code? designing and evaluating screening questions for online surveys with programmers. In: 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), IEEE, pp 537–548
2021
Cited alongside, same era.
Gou J, Yu B, Maybank SJ, et al (2021) Knowledge distillation: A survey. International Journal of Computer Vision 129:1789–1819
2021
Cited alongside, same era.
Zhai X, Kolesnikov A, Houlsby N, et al (2022) Scaling vision transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12104–12113
2022
Later among the works it cites.
Chang TA, Bergen BK (2023) Language model behavior: A comprehensive survey. arXiv preprint arXiv:230311504
2023
Closest in time.
Chen Z, Jiang F, Chen J, et al (2023) Phoenix: Democratizing chatgpt across languages. arXiv preprint arXiv:230410453
2023
Closest in time.
Derner E, Batistič K (2023) Beyond the safeguards: Exploring the security risks of chatgpt. arXiv preprint arXiv:230508005
2023
Closest in time.
Ghorbani A, Cassee N, Robinson D, et al (2023) Autonomy is an acquired taste: Exploring developer preferences for github bots. In: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE), IEEE, pp 1405–1417
2023
Closest in time.
Gudibande A, Wallace E, Snell C, et al (2023) The false promise of imitating proprietary llms. arXiv preprint arXiv:230515717
2023
Closest in time.
Hsieh CY, Li CL, Yeh CK, et al (2023) Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes. arXiv preprint arXiv:230502301
2023
Closest in time.
Ma W, Liu S, Wang W, et al (2023) The scope of chatgpt in software engineering: A thorough investigation. arXiv preprint arXiv:230512138
2023
Closest in time.
Schaeffer R, Miranda B, Koyejo S (2023) Are emergent abilities of large language models a mirage? arXiv preprint arXiv:230415004
2023
Closest in time.
Su CY, Bansal A, Jain V, et al (2023) A language model of java methods with train/test deduplication. In: 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Demonstrations (FSE’23 Demos)
2023
Closest in time.
Sun W, Fang C, You Y, et al (2023) Automatic code summarization via chatgpt: How far are we? arXiv preprint arXiv:230512865
2023
Closest in time.
Tang Y, da Costa AAB, Zhang J, et al (2023) Domain knowledge distillation from large language model: An empirical study in the autonomous driving domain. arXiv preprint arXiv:230711769
2023
Closest in time.
Xu C, Xu Y, Wang S, et al (2023) Small models are valuable plug-ins for large language models. arXiv preprint arXiv:230508848
2023
Closest in time.
Yu Y, Zhuang Y, Zhang J, et al (2023) Large language model as attributed training data generator: A tale of diversity and bias. arXiv preprint arXiv:230615895
2023
Closest in time.
Zhang R, Han J, Zhou A, et al (2023) Llama-adapter: Efficient fine-tuning of language models with zero-init attention. Parameters 7:13B
2023
Closest in time.
Loyola P, Marrese-Taylor E, Matsuo Y (2017) A neural architecture for generating natural language descriptions from source code changes. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Vancouver, Canada, pp 287–292, 10.18653/v1/P17-2045 , URL https://aclanthology.org/P17-2045
2045
Closest in time.
Iyer S, Konstas I, Cheung A, et al (2016) Summarizing source code using a neural attention model. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Berlin, Germany, pp 2073–2083, 10.18653/v1/P16-1195 , URL https://aclanthology.org/P16-1195
2083
Closest in time.