activity
20202026
most citedFairness Testing: A Comprehensive Survey and Analysis of Trends

29 citations · 111 across the 16 of their papers we have counts for

collaborators

20 papers

cs.SE2026

Characterizing the Landscape of Open-Source Satellite Software

Jinfeng Wen, Qi Liang, Yuehan Sun +4

Satellites have become fundamental components of modern technological systems, supporting critical infrastructure in communication, navigation, Earth observation, and scientific re…

cs.LG2023★ 2 cited

Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement

Saurabhsingh Rajput, Tim Widmayer, Ziyuan Shang +3

With the increasing usage, scale, and complexity of Deep Learning (DL) models, their rapidly growing energy consumption has become a critical concern. Promoting green development a…

cs.LG2023★ 4 cited

Fairness Improvement with Multiple Protected Attributes: How Far Are We?

Zhenpeng Chen, Jie M. Zhang, Federica Sarro +1

Existing research mostly improves the fairness of Machine Learning (ML) software regarding a single protected attribute at a time, but this is unrealistic given that many users hav…

cs.SE2023

SCOPE: Performance Testing for Serverless Computing

Jinfeng Wen, Zhenpeng Chen, Jianshu Zhao +5

Serverless computing is a popular cloud computing paradigm that has found widespread adoption across various online workloads. It allows software engineers to develop cloud applica…

cs.SE2023

Assess and Summarize: Improve Outage Understanding with Large Language Models

Pengxiang Jin, Shenglin Zhang, Minghua Ma +13

Cloud systems have become increasingly popular in recent years due to their flexibility and scalability. Each time cloud computing applications and services hosted on the cloud are…

cs.SE2023★ 1 cited

Unveiling Overlooked Performance Variance in Serverless Computing

Jinfeng Wen, Zhenpeng Chen, Federica Sarro +1

Serverless computing is an emerging cloud computing paradigm for developing applications at the function level, known as serverless functions. Due to the highly dynamic execution e…