To conclude, DTM Data Generator for JSON is an intuitive and reliable application that can successfully assist users in creating JSON sample files to employ in tests or other development endeavors. Moreover, DTM Data Generator for JSON supports working with scripts, but requires users to establish a database connection beforehand, by specifying the server and the authentication credentials, along with several other details. Finally, they can choose the destination folder and the total number of files that they want to obtain, then press the ‘Run’ button. Before outputting the documents, users can preview them in the dedicated tab. ![]() The node can be repeated for a preferred amount of times, as well as the value. In order to create a JSON document, users need only add the nodes and subnodes that they want into the file, selecting the ‘Generator’ type from the eponymous menu, for instance ‘Address’, ‘State’, ‘City’, ‘Random Time’, ‘Random Date’, ‘Company’, ‘By Example’, ‘Web Site’, ‘IP Address’ or ‘Custom Generator’. Effortlessly define the structure of a JSON file and output as many as needed The main window enables users to begin building their JSON project or open an existing one, stored on their computer, for further customization. The program features a fairly simple and straightforward appearance, its tabbed interface making it approachable for anyone, no matter the level of experience with similar tools. The utility allows you to generate JSON data, and export the generated JSON files. Schemas, JSON, Data Generation, Synthetic Data, DataGen, DSL, Dataset, Grammar, Randomization, Open Source, Data Science, REST API, PEG.DTM Data Generator for JSON is an effective and user-friendly software solution whose main function resides in automatically creating JSON files, which QA professionals can use in their testing or development work. This tool allows you to generate random JSON files from a template. Its purpose is to parse schema files and generate corresponding DSL models, effectively translating the JSON specification to a DataGen model, then using the original application as a middleware to generate the final datasets.īibTeX - Entry = ![]() This new platform builds upon its prior version and acts as its complement, operating jointly and sharing the same data layer, in order to assure the compatibility of both platforms and the portability of the created DSL models between them. The objective of this new product, DataGen From Schemas, is to expand DataGen’s use cases and raise the datasets specification’s abstraction level, making it possible to generate synthetic datasets directly from schemas. DataGen is able to parse these models and generate synthetic datasets according to the structural and semantic restrictions stipulated, automating the whole process of data generation with spontaneous values created in runtime and/or from a library of support datasets. This paper focuses solely on the JSON Schema component of the application.ĭataGen’s prior version is an online open-source application that allows the quick prototyping of datasets through its own Domain Specific Language (DSL) of specification of data models. This new version of DataGen is an application that makes it possible to automatically generate representative synthetic datasets from JSON and XML schemas, in order to facilitate tasks such as the thorough testing of software applications and scientific endeavors in relevant areas, namely Data Science. This document describes the steps taken in the development of DataGen From Schemas.
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