Generating Privacy Stories From Software Documentation
This addresses privacy compliance issues in software development by automating requirement generation, though it is an incremental application of existing LLM techniques.
The paper tackles the problem of extracting privacy behaviors from software documentation and generating privacy requirements as user stories, achieving F1 scores over 0.8 with models like GPT-4o and Llama 3.
Research shows that analysts and developers consider privacy as a security concept or as an afterthought, which may lead to non-compliance and violation of users' privacy. Most current approaches, however, focus on extracting legal requirements from the regulations and evaluating the compliance of software and processes with them. In this paper, we develop a novel approach based on chain-of-thought prompting (CoT), in-context-learning (ICL), and Large Language Models (LLMs) to extract privacy behaviors from various software documents prior to and during software development, and then generate privacy requirements in the format of user stories. Our results show that most commonly used LLMs, such as GPT-4o and Llama 3, can identify privacy behaviors and generate privacy user stories with F1 scores exceeding 0.8. We also show that the performance of these models could be improved through parameter-tuning. Our findings provide insight into using and optimizing LLMs for generating privacy requirements given software documents created prior to or throughout the software development lifecycle.