License: 
Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.SLATE.2021.6
URN: urn:nbn:de:0030-drops-144239
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/14423/
Santos, Filipa Alves dos ;
Cardoso, Hugo André Coelho ;
da Cunha e Costa, João ;
Carvalho, Válter Ferreira Picas ;
Ramalho, José Carlos
DataGen: JSON/XML Dataset Generator
Abstract
In this document we describe the steps towards DataGen implementation.
DataGen is a versatile and powerful tool that allows for quick prototyping and testing of software applications, since currently too few solutions offer both the complexity and scalability necessary to generate adequate datasets in order to feed a data API or a more complex APP enabling those applications testing with appropriate data volume and data complexity.
DataGen core is a Domain Specific Language (DSL) that was created to specify datasets. This language suffered several updates: repeating fields (with no limit), fuzzy fields (statistically generated), lists, highorder functions over lists, custom made transformation functions. The final result is a complex algebra that allows the generation of very complex datasets coping with very complex requirements. Throughout the paper we will give several examples of the possibilities.
After generating a dataset DataGen gives the user the possibility to generate a RESTFull data API with that dataset, creating a running prototype.
This solution has already been used in real life cases, described with more detail throughout the paper, in which it was able to create the intended datasets successfully. These allowed the application’s performance to be tested and for the right adjustments to be made.
The tool is currently being deployed for general use.
BibTeX - Entry
@InProceedings{santos_et_al:OASIcs.SLATE.2021.6,
author = {Santos, Filipa Alves dos and Cardoso, Hugo Andr\'{e} Coelho and da Cunha e Costa, Jo\~{a}o and Carvalho, V\'{a}lter Ferreira Picas and Ramalho, Jos\'{e} Carlos},
title = {{DataGen: JSON/XML Dataset Generator}},
booktitle = {10th Symposium on Languages, Applications and Technologies (SLATE 2021)},
pages = {6:1--6:14},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-202-0},
ISSN = {2190-6807},
year = {2021},
volume = {94},
editor = {Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Sim\~{o}es, Alberto and Portela, Filipe and Pereira, Maria Jo\~{a}o},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2021/14423},
URN = {urn:nbn:de:0030-drops-144239},
doi = {10.4230/OASIcs.SLATE.2021.6},
annote = {Keywords: JSON, XML, Data Generation, Open Source, REST API, Strapi, JavaScript, Node.js, Vue.js, Scalability, Fault Tolerance, Dataset, DSL, PEG.js, MongoDB}
}
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Keywords: |
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JSON, XML, Data Generation, Open Source, REST API, Strapi, JavaScript, Node.js, Vue.js, Scalability, Fault Tolerance, Dataset, DSL, PEG.js, MongoDB |
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Collection: |
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10th Symposium on Languages, Applications and Technologies (SLATE 2021) |
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Issue Date: |
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2021 |
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Date of publication: |
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10.08.2021 |
