Registration tests
Fill forms with names, documents, emails, phone numbers and addresses to validate required fields, masks and error messages.
Dev/QA dataset builder
Mock Data Generator for Software Testing
Create multiple fictional records to populate systems, validate imports, prototype screens and test QA flows.
Fictional data for testing, QA, and development. Do not use it for fraud or any unlawful purpose.
Generated locally in your browser
Dev / QA / Fictional data
Fictional test data for development and QA
Utinix Mass Data is designed for teams that need to create fictional records quickly while keeping the workflow organized and technically useful. Instead of generating isolated values, you can build datasets for registration tests, import validation, screen prototypes, API payloads, seeders and QA routines. This helps simulate realistic scenarios without relying on improvised spreadsheets, old databases or customer data.
The simple registration, e-commerce and CRM presets provide a practical starting point for common product flows: users, contacts, orders, customers, companies, documents, emails, phone numbers and addresses. From there, you can adjust the quantity, fields and data mode to generate valid, invalid or mixed records, which is essential for testing error messages, form validation, filters, integrations and import processes.
Generation happens in the browser and uses fictional data. The goal is to support development, staging, automated tests and demos without exposing real information. You can export the result in formats that fit daily engineering work, including JSON, NDJSON, CSV, SQL INSERT and Laravel Seeder.
When to use this tool
Use Mass Data whenever you need to validate system behavior with volume, variation and controlled scenarios.
Checkout and e-commerce
Create simulated customers, contacts and records to test purchase flows, order imports, filters and admin lists.
CRM and support
Build lead, company and contact datasets to validate pipelines, segmentation, search, pagination and qualification rules.
ERP and CSV import
Generate files to test initial loads, column mapping, rejected rows and operational reports.
APIs and automation
Use JSON or NDJSON as test payloads for endpoints, mocks, queues, integrations and internal scripts.
Cypress, Playwright and Laravel Seeder
Turn the dataset into fixtures, support data or seeds for repeatable development and QA scenarios.
Valid and invalid data
Testing only with valid data often hides real problems. A professional QA flow should cover correct inputs, clearly invalid inputs and mixed combinations, such as a valid document with a malformed email, incomplete phone number, inconsistent postal code or empty required fields.
This variation helps reveal issues in front-end validation, back-end rules, importers, user-facing messages and API error handling. It also supports edge cases such as duplicates, unexpected formats, incomplete records and data that passes one layer but fails in another.
Privacy and security
Generated records are fictional and should be used only for testing, development, staging, prototypes and study. The tool does not replace data governance rules and should not be used to represent real people or companies.
Generation runs locally in the browser whenever possible, reducing the need to move sensitive content around. Even so, keep fictional datasets separate from real databases, review imports before running them in shared environments and avoid any use that could be confused with real data.
Frequently asked questions
How do I generate test data?
Choose a preset, set the number of records, select the fields you need and generate the dataset. Then export it as JSON, NDJSON, CSV, SQL INSERT or Laravel Seeder depending on where it will be used.
How do I create fictional data for QA?
Start from the flow you want to test, such as registration, checkout, CRM or import. Then generate valid, invalid or mixed data to cover success, validation errors, incomplete fields and unexpected formats.
How do I export SQL test data?
After generating the dataset, enter the table name and copy the SQL INSERT output. It can be used as a starting point for local databases, staging environments or test scripts.
How do I generate valid CPF/CNPJ values?
Fictional documents can follow check digit rules so they pass mathematical validation in test systems. They do not represent real people or companies.
How do I create Laravel seeders with fictional data?
Generate the records, adjust the table name and copy the Laravel Seeder format. You can adapt it for local seeds, development environments or automated QA scenarios.
How can I use fictional data in automation?
Export the dataset as JSON, NDJSON or CSV and use it as a fixture, API payload, import file or base for repeatable tests in tools such as Cypress, Playwright and internal scripts.
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