7 citations · 20 across the 10 of their papers we have counts for
10 papers
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…
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.…
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…
Test suite effectiveness metric evaluation: what do we know and what should we do?
Peng Zhang, Yang Wang, Xutong Liu +6
Comparing test suite effectiveness metrics has always been a research hotspot. However, prior studies have different conclusions or even contradict each other for comparing differe…
Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models
Linghan Meng, Yanhui Li, Lin Chen +4
The boom of DL technology leads to massive DL models built and shared, which facilitates the acquisition and reuse of DL models. For a given task, we encounter multiple DL models a…
Mutant reduction evaluation: what is there and what is missing?
Peng Zhang, Yang Wang, Xutong Liu +6
Background. Many mutation reduction strategies, which aim to reduce the number of mutants, have been proposed. Problem. It is important to measure the ability of a mutation reducti…