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Recent advancements in AI have sparked a trend in constructing large, generalist language models that handle a multitude of tasks, including many code-related ones.
J. R. Landis and G. G. Koch, “An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers,” Biometrics , pp. 363–374, 1977
1977
Earlier work this paper cites.
(2013) Predict Closed Questions on Stack Overflow. [Online]. Available: https://www.kaggle.com/competitions/predict-closed-questions-on-stack-overflow
2013
Earlier work this paper cites.
S. Wang, D. Lo, and L. Jiang, “An empirical study on developer interactions in stackoverflow,” in Proceedings of the 28th annual ACM symposium on applied computing , 2013, pp. 1019–1024
2013
Earlier work this paper cites.
A. Barua, S. W. Thomas, and A. E. Hassan, “What are developers talking about? an analysis of topics and trends in stack overflow,” Empirical Software Engineering , vol. 19, pp. 619–654, 2014
2014
Earlier work this paper cites.
F. Fischer, K. Böttinger, H. Xiao, C. Stransky, Y. Acar, M. Backes, and S. Fahl, “Stack overflow considered harmful? the impact of copy&paste on android application security,” in 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 2017, pp. 121–136
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
I. K. Villanes, S. M. Ascate, J. Gomes, and A. C. Dias-Neto, “What are software engineers asking about android testing on stack overflow?” in Proceedings of the XXXI Brazilian Symposium on Software Engineering , 2017, pp. 104–113
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
N. Meng, S. Nagy, D. Yao, W. Zhuang, and G. A. Argoty, “Secure coding practices in java: Challenges and vulnerabilities,” in Proceedings of the 40th International Conference on Software Engineering , 2018, pp. 372–383
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
C. Ragkhitwetsagul, J. Krinke, M. Paixao, G. Bianco, and R. Oliveto, “Toxic code snippets on stack overflow,” IEEE Transactions on Software Engineering , 2019
2019
Earlier work this paper cites.
C. Sun, X. Qiu, Y. Xu, and X. Huang, “How to fine-tune bert for text classification?” in Chinese Computational Linguistics: 18th China National Conference, CCL 2019, Kunming, China, October 18–20, 2019, Proceedings 18 . Springer, 2019, pp. 194–206
2019
Earlier work this paper cites.
K. Labusch, P. Kulturbesitz, C. Neudecker, and D. Zellhöfer, “Bert for named entity recognition in contemporary and historical german,” in Proceedings of the 15th conference on natural language processing, Erlangen, Germany , 2019, pp. 8–11
2019
Earlier work this paper cites.
H. Zhang, S. Wang, T.-H. P. Chen, Y. Zou, and A. E. Hassan, “An empirical study of obsolete answers on stack overflow,” IEEE Transactions on Software Engineering , 2019
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
F. Liu, G. Li, Y. Zhao, and Z. Jin, “Multi-task learning based pre-trained language model for code completion,” in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering , 2020, pp. 473–485
2020
Earlier work this paper cites.
R. Wang, H. Zhang, G. Lu, L. Lyu, and C. Lyu, “Fret: Functional reinforced transformer with bert for code summarization,” IEEE Access , vol. 8, pp. 135 591–135 604, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
I. Annamoradnejad, J. Habibi, and M. Fazli, “Multi-view approach to suggest moderation actions in community question answering sites,” Information Sciences , vol. 600, pp. 144–154, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0020025522003127
2022
Later among the works it cites.
2022
Later among the works it cites.
D. Bleyl and E. K. Buxton, “Emotion recognition on stackoverflow posts using bert,” in 2022 IEEE International Conference on Big Data (Big Data) . IEEE, 2022, pp. 5881–5885
2022
Later among the works it cites.
J. He, B. Xu, Z. Yang, D. Han, C. Yang, and D. Lo, “Ptm4tag: sharpening tag recommendation of stack overflow posts with pre-trained models,” in Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension , 2022, pp. 1–11
2022
Later among the works it cites.
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2020
Cited alongside, same era.
E. Biswas, M. E. Karabulut, L. Pollock, and K. Vijay-Shanker, “Achieving reliable sentiment analysis in the software engineering domain using bert,” in 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 2020, pp. 162–173
2020
Cited alongside, same era.
R.-M. Karampatsis, H. Babii, R. Robbes, C. Sutton, and A. Janes, “Big code!= big vocabulary: Open-vocabulary models for source code,” in Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering , 2020, pp. 1073–1085
2020
Cited alongside, same era.
P. K. Roy and J. P. Singh, “Predicting closed questions on community question answering sites using convolutional neural network,” Neural Computing and Applications , vol. 32, no. 14, pp. 10 555–10 572, 2020
2020
Cited alongside, same era.
V. J. Hellendoorn and A. A. Sawant, “The growing cost of deep learning for source code,” Communications of the ACM , vol. 65, no. 1, pp. 31–33, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
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,” 2021
2021
Cited alongside, same era.
B. Kou, M. Chen, and T. Zhang, “Automated summarization of stack overflow posts,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 1853–1865
2023
Closest in time.
2023
Closest in time.
(2023) Introducing NVIDIA RTX™ A6000 GPU Instances On Lambda Cloud. [Online]. Available: https://lambdalabs.com/blog/introducing-nvidia-rtx-a6000-gpu-instances-on-lambda-cloud
2023
Closest in time.
2023
Closest in time.
OpenAI, “Gpt-4 technical report,” 2024
2024
Closest in time.
(2024) Stackllama. [Online]. Available: https://huggingface.co/spaces/trl-lib/stack-llama
2024
Closest in time.
R. Rafailov, A. Sharma, E. Mitchell, C. D. Manning, S. Ermon, and C. Finn, “Direct preference optimization: Your language model is secretly a reward model,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
(2024) trl. [Online]. Available: https://github.com/huggingface/trl
2024
Closest in time.
(2024) Beautifulsoup. [Online]. Available: https://pypi.org/project/beautifulsoup4/
2024
Closest in time.
(2024) Figshare. [Online]. Available: https://figshare.com/s/68791d924fb294f16786
2024
Closest in time.
(2025) SOBertBase. [Online]. Available: https://huggingface.co/mmukh/SOBertBase
2025
Closest in time.
(2025) SOBertLarge. [Online]. Available: https://huggingface.co/mmukh/SOBertLarge
2025
Closest in time.
(2023) Nvidia RTX A6000 48GB Review Roundup. [Online]. Available: https://www.pugetsystems.com/labs/articles/nvidia-rtx-a6000-48gb-review-roundup-2063/
2063
Closest in time.