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cs.SE2026
Can Adjusting Hyperparameters Lead to Green Deep Learning: An Empirical Study on Correlations between Hyperparameters and Energy Consumption of Deep Learning Models
Taoran Wang, Yanhui Li, Mingliang Ma +2
Context: Along with developing Deep learning (DL) models, larger datasets and more complex model structures are applied, leading to rising computing resources and energy consumptio…
cs.SE2025
WITNESS: A lightweight and practical approach to fine-grained predictive mutation testing
Zeyu Lu, Peng Zhang, Chun Yong Chong +5
Existing fine-grained predictive mutation testing studies predominantly rely on deep learning, which faces two critical limitations in practice: (1) Exorbitant computational costs.…
cs.SE2023
Toward a consistent performance evaluation for defect prediction models
Xutong Liu, Shiran Liu, Zhaoqiang Guo +7
In defect prediction community, many defect prediction models have been proposed and indeed more new models are continuously being developed. However, there is no consensus on how…