Work

When in Doubt Throw It Out: Building on Confident Learning for Vulnerability Detection

Year: 2025

Type: conference-paper

Abstract: [Context:] Confident learning’s intuition is that a good model can be used to identify mislabelled data. By swapping mislabeled samples that are not confidently predicted, the performance of model can be further improved. [Problem:] Unfortunately, vulnerability detectors are generally under-performing models and confidence learning would conclude that the bulk of the dataset is mislabelled. [New Idea:] We extend confidence learning by identifying a type of training samples that appear in presence of under-performing models: confusing samples. [Emerging Result:] We analyze the formal constraints for confusing samples and perform preliminary experiments that show that the model’s performance is effectively improved after deleting confusing samples entirely from the training set.

Source: IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)

DOI: 10.1109/icse-nier66352.2025.00011

Authors: Yuanjun Gong, Fabio Massacci

Institutions: Renmin University of China, University of Trento, Vrije Universiteit Amsterdam

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Nederlandse Organisatie voor Wetenschappelijk Onderzoek, HORIZON EUROPE Framework Programme

OpenAlex ID: W4411173350

FWCI:

Cites: 14

Cited by:

Related to: 10