The increased popularity and adoption of model-* engineering paradigms, such as model-driven and model-based engineering, leads to an increase in the number of models, metamodels, model transformations and other related artifacts. This calls for automated techniques to analyze large collections of those artifacts to manage model-* ecosystems. SAMOS is a framework to address this challenge: it treats model-* artifacts as data, and applies various techniques - ranging from information retrieval to machine learning - to analyze those artifacts in a holistic, scalable and efficient way. Such analyses can help to understand and manage those ecosystems.

Accepted as original software publication at the journal Science of Computer Programming. 2022. For source code and manual, please go to https://github.com/onderbabur/samos.