The growing adoption of model-driven engineering raised the need for techniques and tools supporting modeling artifacts' reusability. In this respect, several model repositories have been proposed by academia and industry so that modelers can exploit advanced searching facilities to identify reusable artifacts that might fit the particular problem at hand. Despite the enduring quest for the right ways to search and retrieve modeling artifacts, satisfactory solutions are still missing. This paper investigates the adoption of general-purpose indexing and search features provided by Apache Lucene to support the classification and clustering of metamodel repositories. In particular, we show that Apache Lucene allows us to get accurate results whenever the mandatory requirements of more appropriate techniques, such as hierarchical clustering or neural networks, cannot be met.

The growing adoption of model-driven engineering raised the need for techniques and tools supporting modeling artifacts' reusability. In this respect, several model repositories have been proposed by academia and industry so that modelers can exploit advanced searching facilities to identify reusable artifacts that might fit the particular problem at hand. Despite the enduring quest for the right ways to search and retrieve modeling artifacts, satisfactory solutions are still missing. This paper investigates the adoption of general-purpose indexing and search features provided by Apache Lucene to support the classification and clustering of metamodel repositories. In particular, we show that Apache Lucene allows us to get accurate results whenever the mandatory requirements of more appropriate techniques, such as hierarchical clustering or neural networks, cannot be met.

A Lightweight Approach for the Automated Classification and Clustering of Metamodels

Rubei R.;Di Rocco J.;Di Ruscio D.;Pierantonio A.
2021

Abstract

The growing adoption of model-driven engineering raised the need for techniques and tools supporting modeling artifacts' reusability. In this respect, several model repositories have been proposed by academia and industry so that modelers can exploit advanced searching facilities to identify reusable artifacts that might fit the particular problem at hand. Despite the enduring quest for the right ways to search and retrieve modeling artifacts, satisfactory solutions are still missing. This paper investigates the adoption of general-purpose indexing and search features provided by Apache Lucene to support the classification and clustering of metamodel repositories. In particular, we show that Apache Lucene allows us to get accurate results whenever the mandatory requirements of more appropriate techniques, such as hierarchical clustering or neural networks, cannot be met.
978-1-6654-2484-4
The growing adoption of model-driven engineering raised the need for techniques and tools supporting modeling artifacts' reusability. In this respect, several model repositories have been proposed by academia and industry so that modelers can exploit advanced searching facilities to identify reusable artifacts that might fit the particular problem at hand. Despite the enduring quest for the right ways to search and retrieve modeling artifacts, satisfactory solutions are still missing. This paper investigates the adoption of general-purpose indexing and search features provided by Apache Lucene to support the classification and clustering of metamodel repositories. In particular, we show that Apache Lucene allows us to get accurate results whenever the mandatory requirements of more appropriate techniques, such as hierarchical clustering or neural networks, cannot be met.
The growing adoption of model-driven engineering raised the need for techniques and tools supporting modeling artifacts' reusability. In this respect, several model repositories have been proposed by academia and industry so that modelers can exploit advanced searching facilities to identify reusable artifacts that might fit the particular problem at hand. Despite the enduring quest for the right ways to search and retrieve modeling artifacts, satisfactory solutions are still missing. This paper investigates the adoption of general-purpose indexing and search features provided by Apache Lucene to support the classification and clustering of metamodel repositories. In particular, we show that Apache Lucene allows us to get accurate results whenever the mandatory requirements of more appropriate techniques, such as hierarchical clustering or neural networks, cannot be met.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/179960
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