Slides presented at MODEVAR 2024 co-located with VaMoS 2024: https://modevar.github.io/program/ Thanks to Jessie Galasso and Chico Sundermann for the invitation Abstract: "Variability models (e.g., feature models) are widely considered in software engineering research and practice, in order to develop software product lines, configurable systems, generators, or adaptive systems. Numerous success stories of variability models have been reported in different domains (e.g., automotive, aerospace, avionics) and the applicability broadens. However, the use of variability models is not yet universally adopted… Why? Some examples: variability models are not continuously extracted from projects and artefacts; each time a variability model is used, its expressiveness and language should be specialized; learning-based models are decoupled from variability models; modeling software variability should cover different layers and their interactions, etc. The list is arguably opinionated, incomplete, and based on my own practical experience, but also on observations of existing works. I will give 24 reasons to occupy your day as a researcher or practitioner in the field of variability modeling."
Slides: https://github.com/acherm/24RWVMANYU-VaMoS-MODEVAR/blob/main/vamos2024.md (Markdown content) PDF: https://github.com/acherm/24RWVMANYU-VaMoS-MODEVAR/blob/main/VaMoS2024-MODEVAR.pdf (slides in PDF format)