05517nam a22006015i 4500 978-3-031-57327-9 DE-He213 20251030111753.0 cr nn 008mamaa 240329s2024 sz | s |||| 0|eng d 9783031573279 978-3-031-57327-9 10.1007/978-3-031-57327-9 doi QA76.758 UMZ bicssc COM051230 bisacsh UMZ thema Requirements Engineering: Foundation for Software Quality [electronic resource] : 30th International Working Conference, REFSQ 2024, Winterthur, Switzerland, April 8–11, 2024, Proceedings / edited by Daniel Mendez, Ana Moreira. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XVII, 356 p. 105 illus., 69 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Lecture Notes in Computer Science, 1611-3349 ; 14588 Quality models for Requirements Engineering -- How Explainable is Your System? Towards a Quality Model for Explainability -- Identifying relevant Factors of Requirements Quality: an industrial Case Study -- Quality Requirements -- Assessing the Understandability of Attack-Defense Trees for Modelling Security Requirements: an Experimental Investigation -- Learning to Rank Privacy Design Patterns: A Semantic Approach to Meeting Privacy Requirements -- A New Usability Inspection Method: Experience-based Analysis -- Governance-focused Classification of Security and Privacy Requirements from Obligations in Software Engineering Contracts -- Explainability with and in Requirements Engineering -- What Impact do my Preferences Have? A Framework for Explanation-Based Elicitation of Quality Objectives for Robotic Mission Planning -- Candidate Solutions for Defining Explainability Requirements of AI Systems -- Artificial Intelligence for Requirements Engineering -- Opportunities and Limitations of AI in Human-Centered Design - A Research Preview -- A Tertiary Study on AI for Requirements Engineering -- Exploring LLMs’ ability to detect variability in requirements -- Natural Language Processing for Requirements Engineering -- Designing NLP-based solutions for requirements variability management: experiences from a design science study at Visma -- Natural2CTL: A Dataset for Natural Language Requirements and their CTL Formal Equivalents -- Requirements Engineering for Artificial Intelligence -- Towards a Comprehensive Ontology for Requirements Engineering for AI-powered Systems -- Operationalizing Machine Learning Using Requirements-Grounded MLOps -- Crowd-based Requirements Engineering -- Unveiling Competition Dynamics in Mobile App Markets through User Reviews -- Exploring the Automatic Classification of Usage Information in Feedback -- Channeling the Voice of the Crowd: Applying Structured Queries in User Feedback Collection -- Emerging Topics and Challenges in Requirements Engineering -- Requirements Information in Backlog Items: Content Analysis -- Requirements Engineering for No-Code Development (RE4NCD): A Case Study of Rapid Application Development during War -- Behavior-Driven Specification in Practice: An Experience Report -- The Return of Formal Requirements Engineering in the Era of Large Language Models. This book constitutes the refereed proceedings of the 30th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2024, held in Winterthur, Switzerland, during April 8–12, 2024. The 14 full papers and 8 short papers included in this book were carefully reviewed and selected from 59 submissions. They are organized in topical sections as follows: quality models for requirements engineering; quality requirements; explainability with and in requirements engineering; artificial intelligence for requirements engineering; natural language processing for requirements engineering; requirements engineering for artificial intelligence; crowd-based requirements engineering; and emerging topics and challenges in requirements engineering. Software engineering. 33953 Education Data processing. Application software. Machine learning. 55059 Natural language processing (Computer science). 65131 Software Engineering. 33953 Computers and Education. Computer and Information Systems Applications. Machine Learning. 55059 Natural Language Processing (NLP). 66164 Mendez, Daniel. editor. edt http://id.loc.gov/vocabulary/relators/edt 74323 Moreira, Ana. editor. edt http://id.loc.gov/vocabulary/relators/edt 74324 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031573262 Printed edition: 9783031573286 Lecture Notes in Computer Science, 1611-3349 ; 14588 https://doi.org/10.1007/978-3-031-57327-9 ZDB-2-SCS ZDB-2-SXCS ZDB-2-LNC 58784 58784