15 citations · 15 across the 8 of their papers we have counts for
4 papers · 1 filter
Dividable Configuration Performance Learning
Jingzhi Gong, Tao Chen, Rami Bahsoon
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…
Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systems
Chengwen Du, Tao Chen
Background: Fairness testing for deep learning systems has been becoming increasingly important. However, much work assumes perfect context and conditions from the other parts: wel…
Deep Configuration Performance Learning: A Systematic Survey and Taxonomy
Jingzhi Gong, Tao Chen
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 softwar…
Predicting Configuration Performance in Multiple Environments with Sequential Meta-learning
Jingzhi Gong, Tao Chen
Learning and predicting the performance of given software configurations are of high importance to many software engineering activities. While configurable software systems will al…