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In software engineering, the meticulous configuration of software tools is crucial in ensuring optimal performance within intricate systems.
C. E. Rasmussen, “Gaussian processes in machine learning,” in Summer school on machine learning . Springer, 2003, pp. 63–71
2003
Earlier work this paper cites.
K. Wood and E. Pereira, “Impact of misconfiguration in cloud–investigation into security challenges,” International Journal Multimedia and Image Processing , vol. 1, no. 1, pp. 17–25, 2011
2011
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath et al. , “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal processing magazine , vol. 29, no. 6, pp. 82–97, 2012
2012
Earlier work this paper cites.
P. Malo, A. Sinha, P. Korhonen, J. Wallenius, and P. Takala, “Good debt or bad debt: Detecting semantic orientations in economic texts,” Journal of the Association for Information Science and Technology , vol. 65, 2014
2014
Earlier work this paper cites.
K. F. Tomasdottir, M. Aniche, and A. Van Deursen, “Why and how JavaScript developers use linters,” in ASE 2017 - Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering . Institute of Electrical and Electronics Engineers Inc., nov 2017, pp. 578–589
2017
Earlier work this paper cites.
F. Hutter, M. Lindauer, A. Balint, S. Bayless, H. Hoos, and K. Leyton-Brown, “The configurable sat solver challenge (cssc),” Artificial Intelligence , vol. 243, pp. 1–25, 2017
2017
Earlier work this paper cites.
S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “When edge meets learning: Adaptive control for resource-constrained distributed machine learning,” in IEEE INFOCOM 2018-IEEE conference on computer communications . IEEE, 2018, pp. 63–71
2018
Earlier work this paper cites.
I. Standard, “Green AI : Do deep learning frameworks have different costs,” 2019
2019
Earlier work this paper cites.
A. Barbu, D. Mayo, J. Alverio, W. Luo, C. Wang, D. Gutfreund, J. Tenenbaum, and B. Katz, “Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models,” Advances in neural information processing systems , vol. 32, 2019
2019
Earlier work this paper cites.
N. Siegmund, N. Ruckel, and J. Siegmund, “Dimensions of software configuration: on the configuration context in modern software development,” in Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2020, pp. 338–349
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
C. Vassallo, S. Panichella, F. Palomba, S. Proksch, H. C. Gall, and A. Zaidman, “How developers engage with static analysis tools in different contexts,” Empirical Software Engineering , vol. 25, no. 2, pp. 1419–1457, mar 2020. [Online]. Available: https://link.springer.com/article/10.1007/s10664-019-09750-5
2020
Cited alongside, same era.
K. F. Tomasdottir, M. Aniche, and A. Van Deursen, “The Adoption of JavaScript Linters in Practice: A Case Study on ESLint,” IEEE Transactions on Software Engineering , vol. 46, no. 8, pp. 863–891, aug 2020
2020
Cited alongside, same era.
M. Bilal, M. Canini, and R. Rodrigues, “Finding the right cloud configuration for analytics clusters,” in Proceedings of the 11th ACM Symposium on Cloud Computing , 2020, pp. 208–222
2020
Cited alongside, same era.
Y. Zhang, H. He, O. Legunsen, S. Li, W. Dong, and T. Xu, “An evolutionary study of configuration design and implementation in cloud systems,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 2021, pp. 188–200
D. Jin, Z. Jin, Z. Hu, O. Vechtomova, and R. Mihalcea, “Deep learning for text style transfer: A survey,” Computational Linguistics , vol. 48, no. 1, pp. 155–205, 2022
2022
Later among the works it cites.
M. Shafiq and Z. Gu, “Deep residual learning for image recognition: A survey,” Applied Sciences , vol. 12, no. 18, p. 8972, 2022
2022
Later among the works it cites.
C.-j. W. Ramya, R. Udit, G. Bilge, A. Newsha, A. Kiwan, G. Chang, F. Aga, B. James, H. Charles, B. Michael, G. Anurag, M. Ott, A. Melnikov, S. Candido, D. Brooks, G. Chauhan, B. Lee, H.-h. S. L. Bugra, A. Max, B. Joe, S. Ravi, J. Mike, and R. Kim, “Sustainable AI: Environmental Implications, Challenges and Opportunities,” 2022
2022
Later among the works it cites.
A. Hazourli, “Financialbert-a pretrained language model for financial text mining,” Technical report, Tech. Rep., 2022
2022
Later among the works it cites.
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2021
Cited alongside, same era.
Z. Wan, X. Xia, D. Lo, and G. C. Murphy, “How does machine learning change software development practices?” IEEE Transactions on Software Engineering , vol. 47, no. 9, pp. 1857–1871, 2021
2021
Cited alongside, same era.
S. Fahmy, A. Deraman, J. Yahaya, and A. R. Hamdan, “Human competency assessment for software configuration management,” Annals of Emerging Technologies in Computing (AETiC) , vol. 5, no. 5, pp. 69–78, 2021
2021
Cited alongside, same era.
J. Kannan, S. Barnett, L. Cruz, A. Simmons, and A. Agarwal, “Mlsmellhound: a context-aware code analysis tool,” in Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results , 2022, pp. 66–70
2022
Cited alongside, same era.
M. S. Iqbal, R. Krishna, M. A. Javidian, B. Ray, and P. Jamshidi, “Unicorn: reasoning about configurable system performance through the lens of causality,” in Proceedings of the Seventeenth European Conference on Computer Systems , 2022, pp. 199–217
2022
Cited alongside, same era.
M. Casimiro, P. Romano, D. Garlan, and L. Rodrigues, “Towards a Framework for Adapting Machine Learning Components,” Proceedings - 2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2022 , pp. 131–140, 2022
2022
Cited alongside, same era.
Y. Xiao, I. Beschastnikh, Y. Lin, R. S. Hundal, X. Xie, D. S. Rosenblum, and J. S. Dong, “Self-checking deep neural networks for anomalies and adversaries in deployment,” IEEE Transactions on Dependable and Secure Computing , 2022
2022
Cited alongside, same era.
2022
Later among the works it cites.
2022
Later among the works it cites.
G. Blumenschein, “Monitoring builds in a devops infrastructure/submitted by georg blumenschein,” 2023
2023
Closest in time.
M. A. Langford, K. H. Chan, J. E. Fleck, P. K. McKinley, and B. H. Cheng, “Modalas: addressing assurance for learning-enabled autonomous systems in the face of uncertainty,” Software and Systems Modeling , pp. 1–21, 2023
2023
Closest in time.
J. Gesi, X. Shen, Y. Geng, Q. Chen, and I. Ahmed, “Leveraging feature bias for scalable misprediction explanation of machine learning models,” in Proceedings of the 45th International Conference on Software Engineering (ICSE) , 2023
2023
Closest in time.