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

Mengerink, Josh G. M.; Noten, Jeroen; Serebrenik, Alexander

doi:10.1007/s10664-018-9641-6

Cited by 9 publications

(2 citation statements)

References 43 publications

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“…They observed that such benchmarks would encourage the development of new OCL tools. Similar repositories have since appeared in (Noten et al 2017;Mengerink et al 2019).…”

Section: Related Workmentioning

confidence: 96%

Constraints Specification Via Tool Support: A Controlled Experiment.

Maraee¹,

Nachmani²,

Sturm³

2020

JOT

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Models can be used for various purposes such as communication, documentation, design means, and for the generation of various artifacts including code. Nevertheless, as some ambiguities still exist in models, additional languages are required. To address this need, in the context of object-oriented modeling, the Object Constraint Language (OCL) was devised. Alternately, other languages including programming languages can be used for constraint specification. In this work we conducted a controlled experiment using USE and a Java framework we developed for that purpose, and compare the effectiveness of developing model-based constraints with respect to quality, time, and confidence. The results indicate that as Java is more familiar to the subjects than OCL, the time to develop the constraints utilizing the developed framework was shorter whilst the confidence was higher. However, despite the greater familiarity with Java, the constraints quality was better when using OCL and USE.

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“…They observed that such benchmarks would encourage the development of new OCL tools. Similar repositories have since appeared in (Noten et al 2017;Mengerink et al 2019).…”

Section: Related Workmentioning

confidence: 96%

Constraints Specification Via Tool Support: A Controlled Experiment.

Maraee¹,

Nachmani²,

Sturm³

2020

JOT

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“…Although the meta-models are not labelled, it comes with an analysis tool chain to facilitate experiments. A large dataset of OCL expressions is contributed in [37]. The goal is to enhance OCL-related research, which is demonstrated by replicating several studies about OCL with the dataset.…”

Section: Datasetsmentioning

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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