The claim_number function within the mock-jutsu library is an essential tool for developers and QA engineers who require high-quality, structured test data for insurance and legal applications. By generating standardised identifiers, this function ensures that your development environment remains populated with realistic records without compromising sensitive production information. Using the mock-jutsu framework allows teams to automate the creation of these identifiers across various testing phases, from initial unit tests to complex integration workflows, ensuring that every claim-related field is populated with syntactically correct values.
Technically, the claim_number generator adheres to a rigorous format: CLM-YYYYMMDD-NNNNN. This structure combines a fixed "CLM" prefix with a date-based timestamp and a unique five-digit numeric sequence. This specific algorithm mirrors real-world compliance standards used by major insurance providers to facilitate easy sorting, chronological tracking, and historical auditing. Whether you are invoking the tool via the Python API using jutsu.generate('claim_number') or through the command-line interface with the mockjutsu generate claim_number command, the output remains consistent and valid for your database schemas and validation logic.
For performance testers, the seamless integration with JMeter via the ${__mockjutsu(claim_number,)} syntax is particularly beneficial. It allows for the rapid injection of unique mock data into load tests, simulating a high volume of insurance claims without the risk of data collisions. This is crucial for verifying the robustness of claims processing engines and ensuring that search and retrieval functionalities operate correctly under stress. By using this function, developers can bypass the tedious manual creation of spreadsheets and instead rely on programmatic, reproducible test data that scales with their project requirements.
Beyond simple data generation, the claim_number function supports better compliance and data privacy practices within the enterprise. By substituting real customer identifiers with synthetic mock data, organisations can significantly reduce the risk of data breaches during the software development lifecycle. The mock-jutsu library provides the flexibility needed to build reliable, scalable, and secure applications, making it a cornerstone for modern DevOps practices in the financial services sector. This ensures that every test scenario, from UI validation to backend processing, is handled with the highest degree of realism and professional rigour.
mockjutsu generate claim_numbermockjutsu bulk claim_number --count 10mockjutsu export claim_number --count 10 --format jsonmockjutsu export claim_number --count 10 --format csvmockjutsu export claim_number --count 10 --format sqlfrom mockjutsu import jutsujutsu.generate('claim_number')jutsu.bulk('claim_number', count=10)jutsu.template(['claim_number'], count=5)${__mockjutsu_compliance(claim_number)}# JMeter Function: __mockjutsu_compliance# Parameter 1: claim_number# Parameter 2: (not required for this function)GET /generate/claim_number# → {"type":"claim_number","result":"...","status":"ok"}GET /bulk/claim_number?count=10POST /template {"types":["claim_number"],"count":1}