Systematic Recovery of MDE Technology Usage

Rocco, Juri Di; Ruscio, Davide Di; Härtel, Johannes; Iovino, Ludovico; Lämmel, Ralf; Pierantonio, Alfonso

doi:10.1007/978-3-319-93317-7_5

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…The works in 43,44 present tools and techniques to support the discovery of modeling repositories, with specific heuristics able to find relationships between artifacts. These approaches cover the discovery activity, whereas the quality evaluation must be implemented separately.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Understanding the relationship is fundamental since to know if an artifact is related to another, the modeler must scrutinize them, and in some cases, these relationships can be hidden or less visible, depending on the technical space. 44 A non-exhaustive list of possible relationships has been identified in. 43 As output, the artifact discoverer should create a representation of the artifacts that is machine-processable by the subsequent phases of the process.…”

Section: Artifact Discoverermentioning

confidence: 99%

“…Given a model repository, the artifact discoverer component is in charge of discovering the set of artifacts in the input dataset, its associated nature, and the relationships among the existing artifacts. Understanding the relationship is fundamental since to know if an artifact is related to another, the modeler must scrutinize them, and in some cases, these relationships can be hidden or less visible, depending on the technical space 44 . A non‐exhaustive list of possible relationships has been identified in 43 .…”

Section: Proposed Analysis Approachmentioning

confidence: 99%

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AMINO: A quality assessment framework for modeling ecosystems

Di Ruscio,

Iovino,

Pierantonio

2023

J Software Evolu Process

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SummaryModels are core assets in Model‐Driven Engineering (MDE). They are pervasively used throughout software development processes to leverage automation, increase cost‐effectiveness, and enhance quality factors. Model repositories have been successfully proposed to enforce reuse and elicit correlations among modeling artifacts, enabling storing operations on model‐based artifacts and collaborative modeling features. Maintaining and improving the quality of modeling artifacts is mandatory for software quality scale‐ups. Limiting the exploration of datasets and repositories of models to individual artifacts might reduce the ability to capture insights and reuse opportunities. This paper proposes AMINO, an analytics tool for model repositories supporting the discovery and quality evaluation of modeling ecosystems.

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Section: Background and Motivationmentioning

confidence: 99%

Section: Artifact Discoverermentioning

confidence: 99%