Modern Approaches to Software Vulnerability Detection: A Survey of Machine Learning, Deep Learning, and Large Language Models

Abstract

Software vulnerabilities pose significant risks to the security and reliability of modern systems, making automated vulnerability detection an essential research area. Traditional static and rule-based approaches are limited in scalability and adaptability, motivating the adoption of data-driven methods. In this survey, we present a comprehensive review of Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs) techniques for vulnerability detection. We analyze recent advances in feature representation, fine-tuning strategies, generative approaches, and prompt engineering, while highlighting their ability to capture both syntactic and semantic properties of source code. Furthermore, we examine commonly used evaluation metrics and provide a critical discussion of key challenges, including the lack of large-scale real-world datasets, limited vulnerability coverage, class imbalance, interpretability gaps, hallucination, and high computational costs. To address these issues, we outline promising future research directions, such as neuro-symbolic hybrid methods, parameter-efficient fine-tuning, continual learning, cross-language generalization, and explainable AI for vulnerability detection. Unlike previous studies, the present work explores learning paradigms from ML to LLMs using comprehensive evaluation criteria that highlight analytical capability, feature interpretability, and code-context comprehension. By combining these factors, our study addresses the methodological gap between classic feature-based approaches and current LLM-driven reasoning frameworks, providing beneficial insights to develop robust, scalable, and trustworthy software vulnerability detection systems.