As part of a digitized and globalized world, companies tenaciously continue adapting software-based products and services to not only fit their customers’ requirements, but also to fit the market’s needs and keep pace with current technological developments. In this regard, when it comes to highly configurable software systems, it is essential to be able to apply techniques and tools to manage variability. When it comes to studying computer science, ‘variability modeling’ has been an area of focus since the 90s.

In 2012, JKU professors Paul Grünbacher and Rick Rabiser published a paper about software variability together with colleagues at the University of Waterloo (Canada), the University of Hildesheim (Germany), and IT University Copenhagen (Denmark). Now, ten years later, the paper was presented with the "Most Influential Paper Award" at the VaMoS conference. The award was presented at this year’s conference at the end of February (https://vamos2022.isti.cnr.it/, opens an external URL in a new window).

Titled "Cool Features and Tough Decisions: A Comparison of Variability Modeling Approaches", the authors' paper systematically compares two of the most widely used modeling approaches in the area of software variability - namely feature modeling and decision modeling - and identify similarities to well-known approaches when it comes to real-world applications, such as how the Linux operation system manages variability.