In the realm of enterprise software development, generating realistic identity identifiers is a critical requirement for rigorous quality assurance. The ein function within the mock-jutsu library is specifically engineered to produce high-fidelity US Employer Identification Numbers (EIN), ensuring that your test data mirrors the structural integrity of official IRS documentation. By providing a standardised nine-digit format—represented as two digits followed by a hyphen and seven subsequent digits (XX-XXXXXXX)—this utility allows developers to populate databases and API responses with plausible corporate identities without compromising sensitive information.
The mock-jutsu implementation of the ein generator strictly adheres to the formatting conventions established for American business entities. This accuracy is paramount when modelling complex financial systems, tax preparation software, or business-to-business (B2B) platforms. Rather than relying on static or improperly formatted strings, mock-jutsu ensures that every ein generated passes basic structural validation checks, which is essential for testing front-end masks, database constraints, and backend parsing logic. This level of detail helps teams identify edge cases early in the development lifecycle, preventing costly errors during production deployments.
Developers can leverage the ein function across a diverse range of testing scenarios. It is particularly beneficial for validating Know Your Business (KYB) onboarding workflows, where the system must handle various corporate identifiers, or for load testing payroll systems that process thousands of unique employer records. Furthermore, using synthetic mock data instead of real corporate identifiers ensures compliance with international data privacy regulations, such as GDPR and CCPA, by eliminating the risk of exposing actual business entities during the testing phase. This makes it an indispensable tool for organisations that prioritise security alongside technical excellence.
One of the primary benefits of using mock-jutsu is its seamless integration across different technical environments. Whether you are scripting complex simulations in Python, executing quick data generation tasks via the CLI, or conducting performance benchmarks in JMeter, the ein function remains consistently accessible. By streamlining the creation of reliable test data, mock-jutsu empowers engineering teams to focus on core logic and feature development, significantly reducing the overhead associated with manual data preparation and environment setup.
mockjutsu generate einmockjutsu bulk ein --count 10mockjutsu export ein --count 10 --format jsonmockjutsu export ein --count 10 --format csvmockjutsu export ein --count 10 --format sql# --mask: regulation-compliant output (PCI DSS / GDPR / KVKK)mockjutsu generate ein --maskmockjutsu bulk ein --count 5 --maskfrom mockjutsu import jutsujutsu.generate('ein')jutsu.bulk('ein', count=10)jutsu.template(['ein'], count=5)# mask=True: regulation-compliant outputjutsu.generate('ein', mask=True)jutsu.bulk('ein', count=5, mask=True)${__mockjutsu_identity(ein)}# JMeter Function: __mockjutsu_identity# Parameter 1: ein# Parameter 2: (not required for this function)# Add 'mask' keyword to get a regulation-compliant masked value${__mockjutsu_identity(ein,mask)}GET /generate/ein# → {"type":"ein","result":"...","status":"ok"}GET /bulk/ein?count=10POST /template {"types":["ein"],"count":1}# mask=true: regulation-compliant outputGET /generate/ein?mask=trueGET /bulk/ein?count=5&mask=true| Parameter | Values | Description |
|---|---|---|
| --mask | true | false | Return a regulation-compliant masked value (PCI DSS, GDPR, KVKK…) |