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pro vyhledávání: '"Gong, Jingzhi"'
Machine/deep learning models have been widely adopted for predicting the configuration performance of software systems. However, a crucial yet unaddressed challenge is how to cater for the sparsity inherited from the configuration landscape: the infl
Externí odkaz:
http://arxiv.org/abs/2409.07629
Autor:
Gong, Jingzhi, Li, Sisi, d'Aloisio, Giordano, Ding, Zishuo, Ye, Yulong, Langdon, William B., Sarro, Federica
Tuning the parameters and prompts for improving AI-based text-to-image generation has remained a substantial yet unaddressed challenge. Hence we introduce GreenStableYolo, which improves the parameters and prompts for Stable Diffusion to both reduce
Externí odkaz:
http://arxiv.org/abs/2407.14982
Autor:
Gong, Jingzhi
Software systems often have numerous configuration options that can be adjusted to meet different performance requirements. However, understanding the combined impact of these options on performance is often challenging, especially with limited real-
Externí odkaz:
http://arxiv.org/abs/2407.02706
Autor:
Gong, Jingzhi, Chen, Tao
Performance is arguably the most crucial attribute that reflects the quality of a configurable software system. However, given the increasing scale and complexity of modern software, modeling and predicting how various configurations can impact perfo
Externí odkaz:
http://arxiv.org/abs/2403.03322
Autor:
Gong, Jingzhi, Chen, Tao
Learning and predicting the performance of given software configurations are of high importance to many software engineering activities. While configurable software systems will almost certainly face diverse running environments (e.g., version, hardw
Externí odkaz:
http://arxiv.org/abs/2402.03183
Autor:
Gong, Jingzhi, Chen, Tao
Predicting the performance of highly configurable software systems is the foundation for performance testing and quality assurance. To that end, recent work has been relying on machine/deep learning to model software performance. However, a crucial y
Externí odkaz:
http://arxiv.org/abs/2306.06651
Autor:
Gong, Jingzhi, Chen, Tao
Learning and predicting the performance of a configurable software system helps to provide better quality assurance. One important engineering decision therein is how to encode the configuration into the model built. Despite the presence of different
Externí odkaz:
http://arxiv.org/abs/2203.15988
Autor:
Wax, Adam, Backman, Vadim, Hu, Sijung, Hou, Jiajin, Zheng, Xiaoyu, Dwyer, Vincent, Elsahar, Yasmin, Barrett, Laura, Gong, Jingzhi, French, Martin, Bjerke, Torbjørn
Publikováno v:
Proceedings of SPIE; March 2024, Vol. 12856 Issue: 1 p1285602-1285602-8
