Understanding and Tackling Label Errors in Deep Learning-Based Vulnerability Detection (Experience Paper)

Abstract

Software system complexity and security vulnerability diversity are plausible sources of the persistent challenges in software vulnerability research. Applying deep learning methods for automatic vulnerability detection has been proven an effective means to complement traditional detection approaches. Unfortunately, lacking well-qualified benchmark datasets could critically restrict the effectiveness of deep learning-based vulnerability detection techniques. Specifically, the long-term existence of erroneous labels in the existing vulnerability datasets may lead to inaccurate, biased, and even flawed results.