A tool-supported approach for assessing the quality of modeling artifacts

Basciani, Francesco; Rocco, Juri Di; Ruscio, Davide Di; Iovino, Ludovico; Pierantonio, Alfonso

doi:10.1016/j.cola.2019.02.003

Cited by 22 publications

(32 citation statements)

References 17 publications

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“…The Quality Engine component implements the approach previously presented [4]. It is supported by a tool, implemented on top of Epsilon [15], for defining quality models underpinning the quality assessment of modeling artifacts.…”

Section: Proposed Toolchainmentioning

confidence: 99%

“…An operative environment is also provided to apply the defined quality models on actual modeling artifacts enabling automated quality assessment. The tool [4] has been completely integrated into the proposed analysis process, which in case of positive validation of the artifact, triggers the subsequent quality evaluation phase. Figure 4 shows a fragment of a quality assessment phase, which has been performed to analyze different metamodel quality attributes including maintainability, understandability, and complexity according to the quality model previously defined [4].…”

Section: Proposed Toolchainmentioning

confidence: 99%

“…The tool [4] has been completely integrated into the proposed analysis process, which in case of positive validation of the artifact, triggers the subsequent quality evaluation phase. Figure 4 shows a fragment of a quality assessment phase, which has been performed to analyze different metamodel quality attributes including maintainability, understandability, and complexity according to the quality model previously defined [4]. The initial setting defines the required quality characteristics to be evaluated and for each evaluated metamodel, it creates an entry in the model.…”

Section: Proposed Toolchainmentioning

confidence: 99%

“…For assessing the accuracy of the proposed detection mechanism, a corpus of ≈100 metamodels were used. A quality assessment approach for metamodels and ATL transformation is presented [5]. The proposed techniques have been experimented on a corpus of 2,380 metamodels and 27 model transformations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An extensible tool-chain for analyzing datasets of metamodels

Barriga

Ruscio

Iovino

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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Metamodels play a crucial role in any modeling environment as they formalize the modeling constructs underpinning the definition of conforming artifacts, including models, model transformations, code generators, and editors. Understanding the structural characteristics and the quality of the metamodels that are available in public repositories before their reuse is a critical task that demands the adoption of different tools, which might not be easy to adopt. Even the selection of metamodels to be used for experimenting with new tools is not straightforward as it involves exploring various sources of information and dig in each metamodel to check its appropriateness for the evaluation of the tool under development. In this paper, we present a dataset of metamodels, which has been collected for experimenting with different approaches conceived by the authors. The dataset has been automatically curated using a toolchain, which has been redesigned post-ante the definition of the proposed approaches to foster its future reuse. CCS CONCEPTS • Software and its engineering → Model-driven software engineering; Software system models; Software system structures; Software organization and properties.

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Section: Proposed Toolchainmentioning

confidence: 99%

Section: Proposed Toolchainmentioning

confidence: 99%

Section: Proposed Toolchainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An extensible tool-chain for analyzing datasets of metamodels

Barriga

Ruscio

Iovino

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

Self Cite

View full text Add to dashboard Cite

show abstract

“…During the last decades, many development aspects have been addressed thoroughly in the field of MDD. Some of the research directions are still in their infancy [66][67][68]. Da Silva tried to present most of the good practices in his survey [3].…”

Section: Summary Of Related Work In Other End-user Features Of Mddmentioning

confidence: 99%

A Tool Support for Model-Driven Development: An Industrial Case Study from a Measurement Domain

Kos¹,

Mernik

Kosar

2019

Applied Sciences

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End-user programming may utilize Domain-Specific Modeling Languages (DSMLs) to develop applications in the form of models, using only abstractions found in a specific problem domain. Indeed, the productivity benefits reported from Model-Driven Development (MDD) are hard to ignore, and a number of MDD solutions are flourishing. However, not all stories from industry on MDD are successful. End-users, without having software development skills, are more likely to introduce software errors than professional programmers. In this study, we propose and encourage other DSML developers to extend the development of DSML with tool support. We believe the programming tools (e.g., debugger, testing tool, refactoring tool) are also needed for end-users to ensure the proper functioning of the products they develop. It is imperative that domain experts are provided with tools that work on the abstraction level that is familiar to them. In this paper, an industrial experience is presented for building various tools for usage in MDD. Debugger, automated testing infrastructure, refactoring, and other tools were implemented for Sequencer, a DSML. Our experience with the implementation of tool support for MDD confirms that these tools are indispensable for end-user programming in practice, and that implementing those tools might not be as costly as expected.

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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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A tool-supported approach for assessing the quality of modeling artifacts

Cited by 22 publications

References 17 publications

An extensible tool-chain for analyzing datasets of metamodels

An extensible tool-chain for analyzing datasets of metamodels

A Tool Support for Model-Driven Development: An Industrial Case Study from a Measurement Domain

AMINO: A quality assessment framework for modeling ecosystems

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