15 citations · 15 across the 8 of their papers we have counts for
9 papers
Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning
Guoming Long, Shihai Wang, Hui Fang +1
Bug reports, encompassing a wide range of bug types, are crucial for maintaining software quality. However, the increasing complexity and volume of bug reports pose a significant c…
Causally Perturbed Fairness Testing
Chengwen Du, Tao Chen
To mitigate unfair and unethical discrimination over sensitive features (e.g., gender, age, or race), fairness testing plays an integral role in engineering systems that leverage A…
Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning
Zezhen Xiang, Jingzhi Gong, Tao Chen
Modern configurable software systems need to learn models that correlate configuration and performance. However, when the system operates in dynamic environments, the workload vari…
Learning Software Bug Reports: A Systematic Literature Review
Guoming Long, Jingzhi Gong, Hui Fang +1
The recent advancement of artificial intelligence, especially machine learning (ML), has significantly impacted software engineering research, including bug report analysis. ML aim…
Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning
Pengzhou Chen, Jingzhi Gong, Tao Chen
To ease the expensive measurements during configuration tuning, it is natural to build a surrogate model as the replacement of the system, and thereby the configuration performance…
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…