Metamodel clone detection with SAMOS

Babur, Önder; Cleophas, Loek; Brand, Mark van den

doi:10.1016/j.cola.2018.12.002

Cited by 28 publications

(19 citation statements)

References 47 publications

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“…Since the current study does a high-level feature comparison of the service composition systems and languages, the next step would be to look deeper into the workflow languages for service composition and do a multi-dimensional language analysis (e.g. feature analysis and clone detection on the metamodels [32]). Workflow patterns [26] can for instance provide the basic set of criteria such as controlflow, data, resource patterns for in-depth analysis.…”

Section: Discussionmentioning

confidence: 99%

A survey on service composition languages

Nikoo

Babur

Brand

2020

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

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In recent years, service-oriented architecture (SOA) has been adopted by industry in developing enterprise systems. Web service composition has been one of the challenging topics in SOA. Numerous approaches have been proposed to tackle this problem. In industry big companies such as Amazon, Netflix, and Uber have developed their own web service composition languages and tools. In academia, on the other hand, there have also been attempts to resolve some of the complexities in web service composition. In this survey we identify and evaluate current prominent service composition languages, and discuss our key findings. After a scan of dozens of service composition systems, 14 systems that used a language-based approach were included in this study. We believe that our findings will help people from industry and academia to learn about some of the major active composition languages and get an overall idea about their commonalities and differences. CCS CONCEPTS • Software and its engineering → Domain specific languages; Orchestration languages.

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Section: Discussionmentioning

confidence: 99%

A survey on service composition languages

Nikoo

Babur

Brand

2020

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

Self Cite

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“…The application of clustering techniques to relatively large collections of models, in particular Ecore meta-models, has been researched in some depth [8,9,11]. However, these models were not made available and the results are barely replicable.…”

Section: Applicationsmentioning

confidence: 99%

“…still relatively recent. For instance, a neural network has been used to classify meta-models into application domains [39], clustering techniques have shown its usefulness in organizing collections of models [9,11], and graph kernels have been proposed as a means to characterize similar models [17].…”

Section: Introductionmentioning

confidence: 99%

ModelSet: a dataset for machine learning in model-driven engineering

López

Izquierdo²,

Cuadrado

2021

Softw Syst Model

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The application of machine learning (ML) algorithms to address problems related to model-driven engineering (MDE) is currently hindered by the lack of curated datasets of software models. There are several reasons for this, including the lack of large collections of good quality models, the difficulty to label models due to the required domain expertise, and the relative immaturity of the application of ML to MDE. In this work, we present ModelSet, a labelled dataset of software models intended to enable the application of ML to address software modelling problems. To create it we have devised a method designed to facilitate the exploration and labelling of model datasets by interactively grouping similar models using off-the-shelf technologies like a search engine. We have built an Eclipse plug-in to support the labelling process, which we have used to label 5,466 Ecore meta-models and 5,120 UML models with its category as the main label plus additional secondary labels of interest. We have evaluated the ability of our labelling method to create meaningful groups of models in order to speed up the process, improving the effectiveness of classical clustering methods. We showcase the usefulness of the dataset by applying it in a real scenario: enhancing the MAR search engine. We use ModelSet to train models able to infer useful metadata to navigate search results. The dataset and the tooling are available at https://figshare.com/s/5a6c02fa8ed20782935c and a live version at http://modelset.github.io.

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“…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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Metamodel clone detection with SAMOS

Cited by 28 publications

References 47 publications

A survey on service composition languages

A survey on service composition languages

ModelSet: a dataset for machine learning in model-driven engineering

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

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