In the landscape of modern software development, access to realistic and compliant test data is paramount for ensuring the reliability of identity-driven applications. The mock-jutsu library addresses this need through its specialised IntlIDs category, featuring the in_aadhaar function. This utility is designed to generate synthetic 12-digit Indian Aadhaar numbers that mirror the structure of official identification issued by the Unique Identification Authority of India (UIDAI). By integrating this function into a development workflow, engineers can simulate real-world data environments without the security risks associated with using actual Personally Identifiable Information (PII).
What sets the in_aadhaar function apart is its commitment to technical accuracy. Rather than producing a sequence of random digits, the function strictly adheres to the Verhoeff algorithm, a checksum formula used to detect errors in numerical sequences. This ensures that every piece of mock data generated by mock-jutsu is mathematically valid, allowing it to pass through complex validation layers, front-end input masks, and database integrity checks. For developers building KYC (Know Your Customer) modules or fintech platforms, this level of precision is essential for verifying that logic triggers and error-handling routines function as intended under realistic conditions.
The versatility of mock-jutsu makes the in_aadhaar function accessible across various environments, catering to different stages of the software development life cycle. Whether you are using the Python API for unit testing, the command-line interface for rapid prototyping, or the JMeter plugin for performance testing, the library provides a consistent experience. This flexibility allows teams to populate large-scale databases with high-fidelity test data in seconds, significantly reducing the time spent on manual data entry or the sanitisation of production datasets. It effectively bridges the gap between development and production environments by providing standardised identifiers.
Ultimately, adopting the in_aadhaar function within your testing suite enhances both productivity and data privacy compliance. By utilising robust mock data, organisations can safeguard sensitive information while maintaining the high standards required for rigorous quality assurance. Mock-jutsu empowers developers to focus on building features and fixing bugs, providing peace of mind that their testing infrastructure is supported by reliable, standardised, and secure identification simulations that meet global development standards.
mockjutsu generate in_aadhaarmockjutsu bulk in_aadhaar --count 10mockjutsu export in_aadhaar --count 10 --format jsonmockjutsu export in_aadhaar --count 10 --format csvmockjutsu export in_aadhaar --count 10 --format sql# --mask: regulation-compliant output (PCI DSS / GDPR / KVKK)mockjutsu generate in_aadhaar --maskmockjutsu bulk in_aadhaar --count 5 --maskfrom mockjutsu import jutsujutsu.generate('in_aadhaar')jutsu.bulk('in_aadhaar', count=10)jutsu.template(['in_aadhaar'], count=5)# mask=True: regulation-compliant outputjutsu.generate('in_aadhaar', mask=True)jutsu.bulk('in_aadhaar', count=5, mask=True)${__mockjutsu_intl_ids(in_aadhaar)}# JMeter Function: __mockjutsu_intl_ids# Parameter 1: in_aadhaar# Parameter 2: (not required for this function)# Add 'mask' keyword to get a regulation-compliant masked value${__mockjutsu_intl_ids(in_aadhaar,mask)}GET /generate/in_aadhaar# → {"type":"in_aadhaar","result":"...","status":"ok"}GET /bulk/in_aadhaar?count=10POST /template {"types":["in_aadhaar"],"count":1}# mask=true: regulation-compliant outputGET /generate/in_aadhaar?mask=trueGET /bulk/in_aadhaar?count=5&mask=true| Parameter | Values | Description |
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| --mask | true | false | Return a regulation-compliant masked value (PCI DSS, GDPR, KVKK…) |