3 citations · 4 across the 4 of their papers we have counts for
9 papers
Reducing the Cost of Training Security Classifier (via Optimized Semi-Supervised Learning)
Rui Shu, Tianpei Xia, Huy Tu +2
Background: Most of the existing machine learning models for security tasks, such as spam detection, malware detection, or network intrusion detection, are built on supervised mach…
DebtFree: Minimizing Labeling Cost in Self-Admitted Technical Debt Identification using Semi-Supervised Learning
Huy Tu, Tim Menzies
Keeping track of and managing Self-Admitted Technical Debts (SATDs) is important for maintaining a healthy software project. Current active-learning SATD recognition tool involves…
FRUGAL: Unlocking SSL for Software Analytics
Huy Tu, Tim Menzies
Standard software analytics often involves having a large amount of data with labels in order to commission models with acceptable performance. However, prior work has shown that s…
Mining Scientific Workflows for Anomalous Data Transfers
Huy Tu, George Papadimitriou, Mariam Kiran +4
Modern scientific workflows are data-driven and are often executed on distributed, heterogeneous, high-performance computing infrastructures. Anomalies and failures in the workflow…
The Changing Nature of Computational Science Software
Huy Tu, Rishabh Agrawal, Tim Menzies
How should software engineering be adapted for Computational Science (CS)? If we understood that, then we could better support software sustainability, verifiability, reproducibili…
Identifying Self-Admitted Technical Debts with Jitterbug: A Two-step Approach
Zhe Yu, Fahmid Morshed Fahid, Huy Tu +1
Keeping track of and managing Self-Admitted Technical Debts (SATDs) are important to maintaining a healthy software project. This requires much time and effort from human experts t…