The ESPRE workshop serves as a multidisciplinary forum for researchers and practitioners interested in advancing Security and Privacy Requirements Engineering. It promotes dialogue at the intersection of security, privacy, and requirements engineering, with a strong emphasis on addressing the evolving needs of users and organizations. The 12th edition of ESPRE featured nine peer-reviewed contributions covering a range of topics, including requirements extraction, legal and regulatory alignment, traceability, and conceptual modelling. Several papers explored the integration of Large Language Models, reflecting the growing role of AI in this domain. This edition of ESPRE underscores the workshop’s ongoing commitment to fostering innovative, context-aware approaches to the engineering of secure and privacy-preserving systems.
In software development, many different artifacts are created during the process. At the beginning, requirements for the respective software are defined and then written down in a specification. This is followed by other artifacts, such as source code, test cases, or various UML diagrams. Different standards, including ISO 26262 for the automotive industry, require that safety and security requirements be explicitly traced for these different artifacts. However, tracing of requirements in source code is very time-consuming, error-prone, and costly. To reduce the effort involved, various approaches have been developed that use different techniques, such as information retrieval or machine learning, to automate this process. However, these approaches also have problems, so that practical use, especially in safety and security domains, is limited. In this paper, we have therefore developed a plugin for VSCode and a new approach based on LLMs to recover trace links between safety and security requirements and source code. Our results show that the used LLMs are capable of performing this task because they have both code and textual understanding. In various combinations, Llama showed satisfying results in terms of precision (0.8).
The concept of responsible human-centered AI refers to the ethical, accountable, and conscious development, deployment, and operation of AI systems. It is dedicated to ensuring that AI systems align with societal and environmental values, legal norms, and regulations while providing transparency, fairness, privacy, and accountability of the solutions. Responsible human-centered AI encompasses a broad range of socio-technical and sustainability concerns, including biases in AI models, data privacy and security, and explainability of AI decisions, along with robust mechanisms for auditing and monitoring systems' post-deployment to address unintended consequences or evolving risks. The goal of the First International Workshop on Requirements Engineering for Accountable and Conscious Human-centered AI (REACH-AI 2025) is to create a platform for an interdisciplinary discourse of researchers and practitioners on this important aspect of the AI impacts on society.
This paper presents a Q&A knowledge extraction method for developer chatroom environments by summarizing their conversations — ConSum4DCR. In the era of AI, the Internet of Things, and cyber-physical systems, software runtime environments have become increasingly diverse and rapidly evolving. As a critical component of collaborative software development, developer chatrooms exhibit dynamic and multifaceted characteristics (e.g., Q&A patterns in technical discussions, integration with external services such as code snippets or links), which drive developers’ demand for efficient information retrieval. To address the unique nature of conversation texts in developer chatrooms, ConSum4DCR employs an extractive text summarization approach. It segments content into paragraphs at the conversation thread level, applies a topic disentanglement algorithm to handle topic shifts, and uses heuristic rules to prune and merge conversation content to form concise conversation summaries. The paper also proposes experiments to evaluate the effectiveness and readability of the summaries. It assesses summary quality based on the performance of automatic text classifiers and engages developers to read the summaries and complete classification tasks, verifying the extent to which the summaries preserve environmental semantics and enhance comprehensibility. Results show that the method performs excellently in terms of both effectiveness and readability.
Welcome to the sixth edition of the REWBAH workshop, a satellite event of the 2025 IEEE International Requirements Engineering Conference (RE’25). The REWBAH workshop fosters discussion related to requirements engineering resulting from the need to build software systems that not only support healthcare, but also promote well-being, encourage patients and the population in general to live according to healthy lifestyle recommendations, and address the specific needs of an aging population. This multidisciplinary workshop brings together practitioners and researchers from relevant disciplines. Among other objectives, REWBAH aims to: i) develop RE approaches that support multiple perspectives of well-being, aging, and health; ii) examine critical factors that enhance the engagement of patients/population and clinicians and ensure that these systems promote well-being; and iii) explore how requirements engineering can be used as a mediator to create impact and value with emerging technologies, thus contributing to the transformation of health and well-being.
