Automated Software Vulnerability Detection via Curriculum Learning

With the development of deep learning, software vulnerability detection methods based on deep learning have achieved great success, which outperform traditional methods in efficiency and precision. At the training stage, all training samples are treated equally and presented in random order. However, in software vulnerability detection tasks, the detection difficulties of different samples vary greatly. Similar to the human learning mechanism following an easy-to-difficult curriculum learning procedure, vulnerability detection models can also benefit from the easy-to-hard curriculums. Motivated by this observation, we introduce curriculum learning for automated software vulnerability detection, which is capable of arranging easy-to-difficult training samples to learn better detection models without any human intervention. Experimental results show that our method achieves obvious performance improvements compared to baseline models.