Feature Models: AI-Driven Design, Analysis and Applications (SpringerBriefs in Computer Science) - Softcover

Über die Autorin bzw. den Autor

Alexander Felfernig is Full Professor at the Graz University of Technology. Together with his colleagues, he focuses on various research areas including recommender systems, knowledge-based configuration, software product lines, model-based diagnosis, and machine learning. Specifically, his research revolves around the utilization of recommender systems and machine learning within configuration and product line contexts, aligning closely with the central theme of the book.

Andreas Falkner is the Principal Key Expert for Configuration & Planning at Siemens' technology field of Data Analytics and Artificial Intelligence. Since 1992 he has been developing product configurators for technical systems of various Siemens divisions. Currently he is involved in projects aiming at improving configuration processes and tools, especially by applying data-driven and generative AI and integrating sustainability metrics over the whole product life cycle.

David Benavides is Full Professor of Software Engineering and leads the Diverso Lab at the University of Seville. He is in the direction board of UVL (Universal Variability Language, a community effort towards a unified language for variability models), UVLHUb (an open science repository for feature models written in UVL)  and flama (a variability analysis tool written in Python).  His main research interests include software product lines, feature modelling, variability-intensive systems, computational thinking and libre and open-source software development.

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This open access book provides a basic introduction to feature modelling and analysis as well as to the integration of AI methods with feature modelling. It is intended as an introduction for researchers and practitioners who are new to the field and will also serve as a state-of-the-art reference to this audience. While focusing on the AI perspective, the book covers the topics of feature modelling (including languages and semantics), feature model analysis, and interacting with feature model configurators. These topics are discussed along the AI areas of knowledge representation and reasoning, explainable AI, and machine learning.