The SPLC’25 MIP Award goes to “FeatureIDE: An extensible framework for feature-oriented software development” by Thomas Thüm, Christian Kästner, Fabian Benduhn, Jens Meinicke, Gunter Saake, and Thomas Leich, which was published in the Science of Computer Programming journal in 2014.

The MIP award committee selected the SPLC’16 paper Using machine learning to infer constraints for product lines, authored by Paul Temple, José Angel Galindo, Mathieu Acher, and Jean-Marc Jézéquel, as the recipient of the VARIABILITY 2026 Most Influential Paper award. Congratulations to the authors.

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Springer will sponsor the awards for bet papers with an overall amount of €1000.