An extensible tool-chain for analyzing datasets of metamodels

Barriga, Angela; Ruscio, Davide Di; Iovino, Ludovico; Nguyen, Phuong T.; Pierantonio, Alfonso

doi:10.1145/3417990.3419626

Cited by 7 publications

(7 citation statements)

References 9 publications

(14 reference statements)

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“…given by the quality models used for the second experiment, which are defined by our experience and already exploited in other papers. 9,67 This threat is also mitigated by using two quality attributes in the first experiment that are confirmed with the visual inspection of the artifacts.…”

Section: Internal Validitymentioning

confidence: 70%

“…Model repositories can also be used to assess the quality of the stored artifacts 4 . However, currently available platforms limit quality evaluations to the single artifact or offer offline evaluation features 8–10 . Software quality standards, e.g., ISO/IEC 25010, are partially applicable to this context since they are intended for complete software products but not for modeling artifacts such as models or metamodel.…”

Section: Introductionmentioning

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: Internal Validitymentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

AMINO: A quality assessment framework for modeling ecosystems

Di Ruscio,

Iovino,

Pierantonio

2023

J Software Evolu Process

Self Cite

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“…For our experiments, we selected a dataset of metamodels publicly available and presented in [3]. This dataset contains 2'417 metamodels collected by crawling online GitHub repositories.…”

Section: Experiments Setupmentioning

confidence: 99%

Supporting safe metamodel evolution with edelta

Bettini

Ruscio

Iovino

et al. 2022

Int J Softw Tools Technol Transfer

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Metamodels play a crucial role in any model-based application. They underpin the definition of models and tools, and the development of model management operations, including model transformations and analysis. Like any software artifacts, metamodels are subject to evolution to improve their quality or implement unforeseen requirements. Metamodels can be defined in terms of existing ones to increase the separation of concerns and foster reuse. However, the induced coupling can give additional evolution complexity, and dedicated support is needed to avoid breaking metamodels defined in terms of those being changed. This paper presents a tool-supported approach that can automatically analyze the available metamodels and alert modelers in case of change operations that can give place to invalid situations like dangling references. The approach has been implemented in the Edelta development environment and successfully applied to metamodels retrieved from a publicly available Ecore models dataset.

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“…As stated in [8,9,16], the available modeling repositories (such as REMODD [60], ATL Zoo [61], MDEForge [62], GenMyModel [63], Img2UML [64] (now called UML-Ninja) or the ones presented in [65] and [66]) contain an amount and diversity of modeling data which is still limited. Future repositories should contain a curated catalog containing a diverse range of modeling data [8], including good and bad examples, model instances, metamodels, transformations, etc.…”

Section: Datamentioning

confidence: 99%

AI-powered model repair: an experience report—lessons learned, challenges, and opportunities

2022

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Artificial intelligence has already proven to be a powerful tool to automate and improve how we deal with software development processes. The application of artificial intelligence to model-driven engineering projects is becoming more and more popular; however, within the model repair field, the use of this technique remains mostly an open challenge. In this paper, we explore some existing approaches in the field of AI-powered model repair. From the existing approaches in this field, we identify a series of challenges which the community needs to overcome. In addition, we present a number of research opportunities by taking inspiration from other fields which have successfully used artificial intelligence, such as code repair. Moreover, we discuss the connection between the existing approaches and the opportunities with the identified challenges. Finally, we present the outcomes of our experience of applying artificial intelligence to model repair.

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An extensible tool-chain for analyzing datasets of metamodels

Cited by 7 publications

References 9 publications

AMINO: A quality assessment framework for modeling ecosystems

AMINO: A quality assessment framework for modeling ecosystems

Supporting safe metamodel evolution with edelta

AI-powered model repair: an experience report—lessons learned, challenges, and opportunities

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