The co_nit function within the mock-jutsu library serves as a specialised tool for developers needing to generate realistic Colombian Tax Identification Numbers, known as the Número de Identificación Tributaria. As a core component of the IntlIDs category, this utility produces a ten-digit string consisting of a base nine-digit sequence followed by a single verification digit. By providing high-quality test data that mirrors real-world administrative identifiers, mock-jutsu ensures that software systems handling Colombian financial or corporate records can be rigorously evaluated without the need for sensitive or actual taxpayer information.
Technical accuracy is paramount when generating mock data for international identifiers. The co_nit generator strictly adheres to the standards set by the Colombian DIAN (Dirección de Impuestos y Aduanas Nacionales), employing the specific Modulo 11 checksum algorithm to calculate the final check digit. This mathematical precision is essential for bypassing front-end validation logic and back-end integrity checks during the software development lifecycle. Instead of using static or sequential numbers that might fail internal validation routines, developers can rely on this function to produce syntactically correct identifiers every time, facilitating smoother automated testing pipelines and more reliable QA processes.
In practical testing scenarios, the co_nit function is invaluable for seeding databases, performing stress tests on ERP systems, or validating API endpoints that require a valid NIT format. Whether you are building a fintech application for the South American market or localising an e-commerce platform, having access to reliable test data allows for the simulation of diverse user profiles and corporate entities. It eliminates the friction of manual data entry and reduces the risk of logic errors occurring during the production phase by identifying edge cases in how tax IDs are parsed, stored, and displayed within the user interface.
Integration of the co_nit utility is seamless across various environments, supporting modern development workflows with ease. Developers can generate values directly via the command line using the mock-jutsu CLI, or incorporate them into Python scripts with a simple call to the generate method. Furthermore, for performance and load testing, the library provides compatibility with JMeter through a dedicated plugin syntax. By automating the creation of these complex identifiers, mock-jutsu empowers engineering teams to focus on building core features rather than the tedious and error-prone task of manual data fabrication.
mockjutsu generate co_nitmockjutsu bulk co_nit --count 10mockjutsu export co_nit --count 10 --format jsonmockjutsu export co_nit --count 10 --format csvmockjutsu export co_nit --count 10 --format sql# --mask: regulation-compliant output (PCI DSS / GDPR / KVKK)mockjutsu generate co_nit --maskmockjutsu bulk co_nit --count 5 --maskfrom mockjutsu import jutsujutsu.generate('co_nit')jutsu.bulk('co_nit', count=10)jutsu.template(['co_nit'], count=5)# mask=True: regulation-compliant outputjutsu.generate('co_nit', mask=True)jutsu.bulk('co_nit', count=5, mask=True)${__mockjutsu_intl_ids(co_nit)}# JMeter Function: __mockjutsu_intl_ids# Parameter 1: co_nit# Parameter 2: (not required for this function)# Add 'mask' keyword to get a regulation-compliant masked value${__mockjutsu_intl_ids(co_nit,mask)}GET /generate/co_nit# → {"type":"co_nit","result":"...","status":"ok"}GET /bulk/co_nit?count=10POST /template {"types":["co_nit"],"count":1}# mask=true: regulation-compliant outputGET /generate/co_nit?mask=trueGET /bulk/co_nit?count=5&mask=true| Parameter | Values | Description |
|---|---|---|
| --mask | true | false | Return a regulation-compliant masked value (PCI DSS, GDPR, KVKK…) |