The quest for open source projects that use UML

Hebig, Regina; Quang, Truong Ho; Chaudron, Michel R. V.; Robles, Gregório; Fernández, Miguel Angel Castro

doi:10.1145/2976767.2976778

Cited by 80 publications

(76 citation statements)

References 17 publications

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“…Further efforts include mining repositories for public models to be used for MDE research, e.g. mining UML models from GitHub in [4] (the Lindholmen dataset), or mining Ecore metamodels by us 1 . In the former case, the number of UML models can go up to 90k.…”

Section: The Expanding Universe Of Mdementioning

confidence: 99%

Models, More Models, and Then a Lot More

Babur

Cleophas

Brand

et al. 2018

Software Technologies: Applications and Foundations

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Section: The Expanding Universe Of Mdementioning

confidence: 99%

Models, More Models, and Then a Lot More

Babur

Cleophas

Brand

et al. 2018

Software Technologies: Applications and Foundations

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“…We chose GitHub, as it is the largest source of open-source in-development software systems. Moreover, previous studies into the usage of modelingrelated technologies (Hebig et al 2016;Kolovos et al 2015) have also used the GitHub data. Finally, by focusing on GitHub we ensure that our data set includes the aforementioned OCL repository of Jordi Cabot that is also hosted on GitHub.…”

Section: Data Sourcementioning

confidence: 99%

“…Using the query extension:ocl NOT context, we conclude that 999 code results would have been missed using the method of Kolovos et al (2015). Hebig et al (2016) used GHTorrent (Gousios and Spinellis 2012), to select 10% of GitHub repositories that are not forks and have a branch that can be downloaded (totaling 1240000 repositories). They then use a sequence of GitHub API calls to extract file lists for all these repositories to subsequently filter out relevant files.…”

Section: Github Searchmentioning

confidence: 99%

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Empowering OCL research: a large-scale corpus of open-source data from GitHub

2018

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Model-driven engineering (MDE) enables the rise in abstraction during development in software and system design. In particular, meta-models become a central artifact in the process, and are supported by various other artifacts such as editors and transformation. In order to define constraints, invariants, and queries on model-driven artifacts, a generic language has been developed: the Object Constraint Language (OCL). In literature, many studies into OCL have been performed on small collections of data, mostly originating from a single source (e.g., OMG standards). As such, generalization of results beyond the data studied is often mentioned as a threat to validity. Creation of a benchmark dataset has already been identified as a key enabler to address the generalization threat. To facilitate further empirical studies in the field of OCL, we present the first large-scale dataset of 103262 OCL expression, systematically extracted from 671 GitHub repositories. In particular, our dataset has extracted these expressions from various types of files (a.o. metamodels and model-to-text transformations). In this work we showcase a variety of different studies performed using our dataset, and describe several other types that could be performed. We extend previous work with data and experiments regarding OCL in model-to-text (mtl) transformations.

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“…We will also apply an off-the-shelf architecture, fine tuning it to our task, to show the advantages of knowledge transfer when working with limited data in the software domain. We focus on the classification of unified modeling language (UML) diagrams into class and sequence diagrams from a publicly-available dataset [5]. This dataset has been previously leveraged to demonstrate barriers that arise when applying deep architectures with vast parameter spaces.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Efficacy of Transfer Learning in Mining Image-Based Software Artifacts

Best

Ott

Linstead

2020

Preprint

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Transfer learning allows us to train deep architectures requiring a large number of learned parameters, even if the amount of available data is limited, by leveraging existing models previously trained for another task.Here we explore the applicability of transfer learning utilizing models pretrained on non-software engineering data applied to the problem of classifying software UML diagrams. Our experimental results show training reacts positively to transfer learning as related to sample size, even though the pre-trained model was not exposed to training instances from the software domain. We contrast the transferred network with other networks to show its advantage on different sized training sets, which indicates that transfer learning is equally effective to custom deep architectures when large amounts of training data is not available.

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The quest for open source projects that use UML

Cited by 80 publications

References 17 publications

Models, More Models, and Then a Lot More

Models, More Models, and Then a Lot More

Empowering OCL research: a large-scale corpus of open-source data from GitHub

Exploring the Efficacy of Transfer Learning in Mining Image-Based Software Artifacts

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