Bridging the Gap Between Mainframe Systems and Modern Software Testing
Many of today’s enterprises still depend on mainframe systems to run core applications that handle vast volumes of mission-critical data. Yet, testing these systems is a persistent challenge due to the complexity of COBOL copybooks, EBCDIC encoding, and interconnected data structures.
GenRocket enables organizations to generate high-quality synthetic test data tailored to the specific needs of mainframe environments while supporting full integration with modern CI/CD pipelines.
Why GenRocket for Mainframe Testing?
GenRocket’s Test Data Automation (TDA) platform is purpose-built to support legacy infrastructure alongside modern systems, making it ideal for organizations looking to streamline testing without compromising data integrity or speed.
Key Capabilities:
- COBOL Copybook Parsing
- Automatically parses COBOL copybooks to model record layouts and data types for synthetic data generation.
- EBCDIC Encoding Support
- Outputs data in EBCDIC format to mirror mainframe-native data, with optional support for ASCII, XML, and JSON for downstream testing.
- MQ Messaging Support
- Generates synthetic or masked MQ message payloads to test transactional workflows involving IBM MQ, supporting mainframe-to-mid-tier communication scenarios.
- Referential Integrity and Relational Data Models
- Maintains complex parent/child and cross-table relationships across generated data to simulate production-like workflows.
- High-Performance Runtime Engine
- Generates millions of records per minute with minimal infrastructure footprint.
- DevOps & CI/CD Integration
- Test data can be provisioned dynamically as part of automated test cycles using GenRocket APIs and integrations.
Mainframe Architecture Support: IBM i and IBM z Systems
IBM i (iSeries/AS400) and IBM z (zSeries) systems are foundational to enterprise computing, transaction processing, and legacy business applications. GenRocket provides flexible test data provisioning solutions tailored to the specific architecture and data workflows of each system, supporting both synthetic data generation and masking.
IBM i (iSeries, AS/400)
- Synthetic Data Approach:
- Synthetic data is designed using COBOL Copybook or JDBC.
- Generates fixed-width, EBCDIC-encoded data for flat/sequential files.
- Creates synthetic messages for IBM MQ or inserts data via JDBC.
- Data Masking Approach:
- Extracts real data from DB2/400 as VSAM or flat files.
- Masks sensitive data using synthetic data replacement in flat files while maintaining format integrity.
- Generates masked message payloads for IBM MQ.
IBM z (zSeries, Mainframes)
- Synthetic Data Approach:
- Synthetic data is designed using COBOL Copybook or JDBC.
- Generates fixed-width, EBCDIC-compatible VSAM/IMS DB datasets.
- Creates test API payloads for CICS Web Services.
- Produces synthetic MQ messages for transaction testing.
- Data Masking Approach:
- Extracts and masks real DB2 for z/OS data from production copy.
- Masks VSAM and IMS DB records while preserving business logic.
- Obfuscates API responses and MQ messages for compliance testing.
Data Format Interoperability
This diagram shows how GenRocket supports various output formats:
Real-World Example: Global Banking System Modernization
A major global bank faced the challenge of testing EBCDIC-based mainframe data transformations for nearly 500 downstream systems. These transformations were essential for regulatory compliance and financial reporting.
Their Challenges:
- Reliance on masked production data was slow and risky
- Manual processes introduced data inconsistencies
- Required referential integrity across legacy and modern systems
GenRocket’s Solution:
- Imported COBOL copybooks to define data structures
- Generated millions of valid EBCDIC records
- Transformed data into downstream formats (ASCII, XML)
- Automated test data delivery to all 500 systems
Results:
- Reduced data provisioning time by 90%
- Improved test coverage and data accuracy
- Integrated seamlessly into existing CI/CD workflows
Summary for IT and QA Leaders
- Accelerate Testing: Cut mainframe test data provisioning from days to minutes.
- Increase Accuracy: Generate synthetic data with full referential integrity.
- Ensure Compliance: Test data flows from mainframe to modern systems confidently.
- Reduce Risk: Eliminate dependency on masked production data.
- Drive Modernization: Support legacy and cloud-native systems with a unified test data strategy.
Download the Mainframe Case Study to learn more.