Noisy Label Learning for Security Defects

Understand

Data-driven software engineering processes, such as vulnerability prediction heavily rely on the quality of the data used.

• In this paper, we observe that it is infeasible to obtain a noise-free security defect dataset in practice.
• Despite the vulnerable class, the non-vulnerable modules are difficult to be verified and determined as truly exploit free given the limited manual efforts available.
• It results in uncertainty, introduces labeling noise in the datasets and affects conclusion validity.