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Hugging Face (HF) has established itself as a crucial platform for the development and sharing of machine learning (ML) models.
E. B. Swanson, “The dimensions of maintenance,” in Proceedings of the 2nd international conference on Software engineering , 1976, pp. 492–497
1976
Earlier work this paper cites.
D. Rowe, J. Leaney, and D. Lowe, “Defining systems evolvability-a taxonomy of change,” Change , vol. 94, pp. 541–545, 1994
1994
Earlier work this paper cites.
V. R. B. G. Caldiera and H. D. Rombach, “The goal question metric approach,” Encyclopedia of software engineering , pp. 528–532, 1994
1994
Earlier work this paper cites.
M. M. Lehman, J. F. Ramil, P. D. Wernick, D. E. Perry, and W. M. Turski, “Metrics and laws of software evolution-the nineties view,” in Proceedings Fourth International Software Metrics Symposium . IEEE, 1997, pp. 20–32
1997
Earlier work this paper cites.
K. H. Bennett and V. T. Rajlich, “Software maintenance and evolution: a roadmap,” in Proceedings of the Conference on the Future of Software Engineering , 2000, pp. 73–87
2000
Earlier work this paper cites.
D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” Journal of machine Learning research , vol. 3, no. Jan, pp. 993–1022, 2003
2003
Earlier work this paper cites.
J. Lu, A. Liu, F. Dong, F. Gu, J. Gama, and G. Zhang, “Learning under Concept Drift: A Review,” IEEE Transactions on Knowledge and Data Engineering , vol. 31, no. 12, pp. 1–1, 2018, _eprint: 2004.05785. [Online]. Available: https://ieeexplore.ieee.org/document/8496795/
2004
Earlier work this paper cites.
R. S. Pressman, Software engineering: a practitioner’s approach . Palgrave macmillan, 2005
2005
Earlier work this paper cites.
T. Zimmermann, S. Kim, A. Zeller, and E. J. Whitehead, “Mining version archives for co-changed lines,” in Proceedings of the 2006 International Workshop on Mining Software Repositories , ser. MSR ’06. New York, NY, USA: Association for Computing Machinery, 2006, p. 72–75. [Online]. Available: https://doi-org.recursos.biblioteca.upc.edu/10.1145/1137983.1138001
2006
Earlier work this paper cites.
V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks,” Journal of statistical mechanics: theory and experiment , vol. 2008, no. 10, p. P10008, 2008
2008
Earlier work this paper cites.
P. E. McKnight and J. Najab, “Mann-whitney u test,” The Corsini encyclopedia of psychology , pp. 1–1, 2010
2010
Earlier work this paper cites.
ISO/IEC 25010, ISO/IEC 25010:2011, Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — System and software quality models , Std., 2011
2011
Earlier work this paper cites.
M. Röder, A. Both, and A. Hinneburg, “Exploring the space of topic coherence measures,” in Proceedings of the eighth ACM international conference on Web search and data mining , 2015, pp. 399–408
2015
Earlier work this paper cites.
E. Schubert, J. Sander, M. Ester, H. P. Kriegel, and X. Xu, “Dbscan revisited, revisited: why and how you should (still) use dbscan,” ACM Transactions on Database Systems (TODS) , vol. 42, no. 3, pp. 1–21, 2017
2017
Earlier work this paper cites.
S. Amershi, A. Begel, C. Bird, R. DeLine, H. Gall, E. Kamar, N. Nagappan, B. Nushi, and T. Zimmermann, “Software Engineering for Machine Learning: A Case Study,” in 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) . IEEE, May 2019, pp. 291–300. [Online]. Available: https://ieeexplore.ieee.org/document/8804457/
2019
Earlier work this paper cites.
2019
Cited alongside, same era.
M. Mitchell, S. Wu, A. Zaldivar, P. Barnes, L. Vasserman, B. Hutchinson, E. Spitzer, I. D. Raji, and T. Gebru, “Model cards for model reporting,” in Proceedings of the conference on fairness, accountability, and transparency , 2019, pp. 220–229
2019
Cited alongside, same era.
J. L. Leevy, T. M. Khoshgoftaar, R. A. Bauder, and N. Seliya, “Investigating the relationship between time and predictive model maintenance,” Journal of Big Data , vol. 7, no. 1, p. 36, Dec. 2020. [Online]. Available: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-020-00312-x
2020
Cited alongside, same era.
M. U. Sarwar, S. Zafar, M. W. Mkaouer, G. S. Walia, and M. Z. Malik, “Multi-label classification of commit messages using transfer learning,” in 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) . IEEE, 2020, pp. 37–42
W. Jiang, N. Synovic, M. Hyatt, T. R. Schorlemmer, R. Sethi, Y.-H. Lu, G. K. Thiruvathukal, and J. C. Davis, “An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . Melbourne, Australia: IEEE, May 2023, pp. 2463–2475. [Online]. Available: https://ieeexplore.ieee.org/document/10172757/
2023
Closest in time.
J. Castaño, S. Martínez-Fernández, X. Franch, and J. Bogner, “Exploring the Carbon Footprint of Hugging Face’s ML Models: A Repository Mining Study,” in ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) . New Orleans, LA, USA: IEEE, 2023
2023
Closest in time.
L. Gong, J. Zhang, M. Wei, H. Zhang, and Z. Huang, “What is the intended usage context of this model? an exploratory study of pre-trained models on various model repositories,” ACM Transactions on Software Engineering and Methodology , vol. 32, no. 3, pp. 1–57, 2023
2023
Closest in time.
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2020
Cited alongside, same era.
J. Coelho, M. T. Valente, L. Milen, and L. L. Silva, “Is this github project maintained? measuring the level of maintenance activity of open-source projects,” Information and Software Technology , vol. 122, p. 106274, 2020
2020
Cited alongside, same era.
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz et al. , “Transformers: State-of-the-art natural language processing,” in Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations , 2020, pp. 38–45
2020
Cited alongside, same era.
J. Tsay, A. Braz, M. Hirzel, A. Shinnar, and T. Mummert, “Aimmx: Artificial intelligence model metadata extractor,” in Proceedings of the 17th International Conference on Mining Software Repositories , ser. MSR ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 81–92. [Online]. Available: https://doi.org/10.1145/3379597.3387448
2020
Cited alongside, same era.
I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Computer Science , vol. 2, no. 3, p. 160, May 2021. [Online]. Available: https://link.springer.com/10.1007/s42979-021-00592-x
2021
Cited alongside, same era.
J. Bogner, R. Verdecchia, and I. Gerostathopoulos, “Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study,” in 2021 IEEE/ACM International Conference on Technical Debt (TechDebt) . IEEE, May 2021, pp. 64–73, arXiv: 2103.09783. [Online]. Available: https://ieeexplore.ieee.org/document/9463054/
2021
Cited alongside, same era.
Y. Tang, R. Khatchadourian, M. Bagherzadeh, R. Singh, A. Stewart, and A. Raja, “An Empirical Study of Refactorings and Technical Debt in Machine Learning Systems,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, May 2021, pp. 238–250. [Online]. Available: https://ieeexplore.ieee.org/document/9401990/
2021
Cited alongside, same era.
M. Dilhara, A. Ketkar, and D. Dig, “Understanding Software-2.0: A Study of Machine Learning Library Usage and Evolution,” ACM Transactions on Software Engineering and Methodology , vol. 30, no. 4, pp. 1–42, Jul. 2021. [Online]. Available: https://dl.acm.org/doi/10.1145/3453478
2021
Cited alongside, same era.
S. Martínez-Fernández, J. Bogner, X. Franch, M. Oriol, J. Siebert, A. Trendowicz, A. M. Vollmer, and S. Wagner, “Software Engineering for AI-Based Systems: A Survey,” ACM Transactions on Software Engineering and Methodology , vol. 31, no. 2, pp. 1–59, Apr. 2022. [Online]. Available: https://dl.acm.org/doi/10.1145/3487043
2022
Cited alongside, same era.
A. Paleyes, R.-G. Urma, and N. D. Lawrence, “Challenges in Deploying Machine Learning: A Survey of Case Studies,” ACM Computing Surveys , vol. 55, no. 6, pp. 1–29, Jul. 2023. [Online]. Available: https://dl.acm.org/doi/10.1145/3533378
2023
Closest in time.
J. Leest, I. Gerostathopoulos, and C. Raibulet, “Evolvability of Machine Learning-based Systems: An Architectural Design Decision Framework,” in 2023 IEEE 20th International Conference on Software Architecture Companion (ICSA-C) . L’Aquila, Italy: IEEE, Mar. 2023, pp. 106–110. [Online]. Available: https://ieeexplore.ieee.org/document/10092638/
2023
Closest in time.
A. Kathikar, A. Nair, B. Lazarine, A. Sachdeva, and S. Samtani, “Assessing the Vulnerabilities of the Open-Source Artificial Intelligence (AI) Landscape: A Large-Scale Analysis of the Hugging Face Platform,” in IEEE Intelligence and Security Informatics . Charlotte, NC, USA: IEEE, Oct. 2023
2023
Closest in time.
A. Ait, J. L. C. Izquierdo, and J. Cabot, “HFCommunity: A Tool to Analyze the Hugging Face Hub Community,” in 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . Taipa, Macao: IEEE, Mar. 2023, pp. 728–732. [Online]. Available: https://ieeexplore.ieee.org/document/10123660/
2023
Closest in time.
A. Anonymous, “Replication Package for ’What is the Evolution and Maintenance of Pre-Trained ML models on Hugging Face?’,” Nov. 2023. [Online]. Available: https://doi.org/10.5281/zenodo.10153155
2023
Closest in time.
2023
Closest in time.
A. Bhat, A. Coursey, G. Hu, S. Li, N. Nahar, S. Zhou, C. Kästner, and J. L. Guo, “Aspirations and practice of ml model documentation: Moving the needle with nudging and traceability,” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems , 2023, pp. 1–17
2023
Closest in time.
F. Lanubile, S. Martínez-Fernández, and L. Quaranta, “Training future ml engineers: a project-based course on mlops,” IEEE software , 2023
2023
Closest in time.
R. Nazir, A. Bucaioni, and P. Pelliccione, “Architecting ML-enabled systems: Challenges, best practices, and design decisions,” Journal of Systems and Software , vol. 207, p. 111860, Jan. 2024. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0164121223002558
2024
Closest in time.
“HfApi Client,” https://huggingface.co/docs/huggingface_hub/package_reference/hf_api , Accessed: 01-02-2024
2024
Closest in time.
“Data Version Control · DVC,” https://dvc.org/ , Accessed: 01-02-2024
2024
Closest in time.
“DagsHub: The Home for Machine Learning Collaboration,” https://dagshub.com/ , Accessed: 01-02-2024
2024
Closest in time.