133 citations · 365 across the 73 of their papers we have counts for
13 papers · 1 filter
On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing
Zeming Dong, Qiang Hu, Zhenya Zhang +5
Graph neural network (GNN)-based graph learning has been popular in natural language and programming language processing, particularly in text and source code classification. Typic…
MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentation
Zeming Dong, Qiang Hu, Yuejun Guo +5
Inspired by the great success of Deep Neural Networks (DNNs) in natural language processing (NLP), DNNs have been increasingly applied in source code analysis and attracted signifi…
Predicting Flaky Tests Categories using Few-Shot Learning
Amal Akli, Guillaume Haben, Sarra Habchi +2
Flaky tests are tests that yield different outcomes when run on the same version of a program. This non-deterministic behaviour plagues continuous integration with false signals, w…
CodeBERT-nt: code naturalness via CodeBERT
Ahmed Khanfir, Matthieu Jimenez, Mike Papadakis +1
Much of software-engineering research relies on the naturalness of code, the fact that code, in small code snippets, is repetitive and can be predicted using statistical language m…
LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity
Martin Gubri, Maxime Cordy, Mike Papadakis +2
We propose transferability from Large Geometric Vicinity (LGV), a new technique to increase the transferability of black-box adversarial attacks. LGV starts from a pretrained surro…
Learning from what we know: How to perform vulnerability prediction using noisy historical data
Aayush Garg, Renzo Degiovanni, Matthieu Jimenez +3
Vulnerability prediction refers to the problem of identifying system components that are most likely to be vulnerable. Typically, this problem is tackled by training binary classif…