47 citations · 84 across the 13 of their papers we have counts for
13 papers
Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms
Organizers of QueerInAI, Nathan Dennler, Anaelia Ovalle +11
Bias evaluation benchmarks and dataset and model documentation have emerged as central processes for assessing the biases and harms of artificial intelligence (AI) systems. However…
Less, but Stronger: On the Value of Strong Heuristics in Semi-supervised Learning for Software Analytics
Huy Tu, Tim Menzies
In many domains, there are many examples and far fewer labels for those examples; e.g. we may have access to millions of lines of source code, but access to only a handful of warni…
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…
How to Find Actionable Static Analysis Warnings: A Case Study with FindBugs
Rahul Yedida, Hong Jin Kang, Huy Tu +3
Automatically generated static code warnings suffer from a large number of false alarms. Hence, developers only take action on a small percent of those warnings. To better predict…
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…
Fair-SSL: Building fair ML Software with less data
Joymallya Chakraborty, Suvodeep Majumder, Huy Tu
Ethical bias in machine learning models has become a matter of concern in the software engineering community. Most of the prior software engineering works concentrated on finding e…
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…
Better Data Labelling with EMBLEM (and how that Impacts Defect Prediction)
Huy Tu, Zhe Yu, Tim Menzies
Standard automatic methods for recognizing problematic development commits can be greatly improved via the incremental application of human+artificial expertise. In this approach,…
Is One Hyperparameter Optimizer Enough?
Huy Tu, Vivek Nair
Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, t…
Can You Explain That, Better? Comprehensible Text Analytics for SE Applications
Amritanshu Agrawal, Huy Tu, Tim Menzies
Text mining methods are used for a wide range of Software Engineering (SE) tasks. The biggest challenge of text mining is high dimensional data, i.e., a corpus of documents can con…