MLAssetSelection

MLAssetSelection is a web application that helps software engineering (SE) researchers and practitioners find and select pre-trained models and datasets for SE tasks. It continuously extracts assets from multiple AI registries and catalogs them against a taxonomy of SE tasks that covers the whole software development life cycle.

By combining structured metadata with domain knowledge, the tool supports:

• Search and filtering of ML assets using multiple criteria (e.g., task, domain, quality attributes).
• Comparison of alternative assets in terms of suitability for a given MLS context.
• Informed selection decisions aligned with the evolution goals of the target system.

Team: Alexandra González, Oscar Cerezo, Xavier Franch, Silverio Martínez-Fernández

Access to Tool