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Learning from What We Know: How to Perform Vulnerability Prediction using Noisy Historical Data
Empirical Software Engineering (EMSE), 2020
21 December 2020
Aayush Garg
Renzo Degiovanni
Matthieu Jimenez
Maxime Cordy
Mike Papadakis
Yves Le Traon
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Papers citing
"Learning from What We Know: How to Perform Vulnerability Prediction using Noisy Historical Data"
6 / 6 papers shown
A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair
ARES (ARES), 2025
Zanis Ali Khan
Aayush Garg
Qiang Tang
313
1
0
05 Jun 2025
Software Vulnerability Analysis Across Programming Language and Program Representation Landscapes: A Survey
Zhuoyun Qian
Fangtian Zhong
Qin Hu
Yili Jiang
Jiaqi Huang
Mengfei Ren
Jiguo Yu
275
0
0
26 Mar 2025
Data Quality Issues in Vulnerability Detection Datasets
Yuejun Guo
Seifeddine Bettaieb
AAML
207
5
0
08 Oct 2024
Automatic Data Labeling for Software Vulnerability Prediction Models: How Far Are We?
Triet Huynh
Muhammad Ali Babar
224
8
0
25 Jul 2024
When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection
International Conference on Automated Software Engineering (ASE), 2023
Xinjie Wen
Xinche Wang
Cuiyun Gao
Shaohua Wang
Yang Liu
Zhaoquan Gu
156
30
0
21 Aug 2023
Vulnerability Mimicking Mutants
Aayush Garg
Renzo Degiovanni
Mike Papadakis
Yves Le Traon
131
4
0
07 Mar 2023
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