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DataGen: JSON/XML Dataset Generator

Authors Filipa Alves dos Santos, Hugo André Coelho Cardoso, João da Cunha e Costa, Válter Ferreira Picas Carvalho, José Carlos Ramalho

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OASIcs.SLATE.2021.6.pdf
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Author Details

Filipa Alves dos Santos
  • University of Minho, Braga, Portugal
Hugo André Coelho Cardoso
  • University of Minho, Braga, Portugal
João da Cunha e Costa
  • University of Minho, Braga, Portugal
Válter Ferreira Picas Carvalho
  • University of Minho, Braga, Portugal
José Carlos Ramalho
  • Department of Informatics, University of Minho, Braga, Portugal

Cite AsGet BibTex

Filipa Alves dos Santos, Hugo André Coelho Cardoso, João da Cunha e Costa, Válter Ferreira Picas Carvalho, and José Carlos Ramalho. DataGen: JSON/XML Dataset Generator. In 10th Symposium on Languages, Applications and Technologies (SLATE 2021). Open Access Series in Informatics (OASIcs), Volume 94, pp. 6:1-6:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/OASIcs.SLATE.2021.6

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.

Subject Classification

ACM Subject Classification
  • Software and its engineering → Domain specific languages
  • Theory of computation → Grammars and context-free languages
  • Information systems → Open source software
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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