Automatic identification of privacy and security requirements: a systematic literature review

Abstract The utmost importance of privacy and security requirements in software development calls for adopting methods that enable the identification and proactive mitigation of these issues during the system development. Our survey of 45 primary studies provides an overview of the methods, document types, and datasets employed in tackling this challenge, along with an analysis of approaches demonstrating superior performance based on document types and specific identification problems. Analysis reveals a wide adoption of ML-based systems on diverse datasets, showcasing the effectiveness of leveraging various sources of information to identify privacy and security requirements in software development.