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Data-driven software engineering processes, such as vulnerability predictionheavily rely on the quality of the data used. In this paper, we observe that itis infeasible to obtain a noise-free security defect dataset in practice.Despite the vulnerable class, the non-vulnerable modules are difficult to beverified and determined as truly exploit free given the limited manual effortsavailable. It results in uncertainty, introduces labeling noise in the datasetsand affects conclusion validity. To address this issue, we propose novellearning methods that are robust to label impurities and can leverage the mostfrom limited label data; noisy label learning. We investigate various noisylabel learning methods applied to software vulnerability prediction.Specifically, we propose a two-stage learning method based on noise cleaning toidentify and remediate the noisy samples, which improves AUC and recall ofbaselines by up to 8.9% and 23.4%, respectively. Moreover, we discuss severalhurdles in terms of achieving a performance upper bound with semi-omniscientknowledge of the label noise. Overall, the experimental results show thatlearning from noisy labels can be effective for data-driven software andsecurity analytics.

Noisy Label Learning for Security Defects