Our team is evaluating testing strategies for risk matrix functionality in TrackWise 9.1. We’ve been doing manual exploratory testing for risk assessments, but as our risk management module grows more complex with custom scoring algorithms and matrix configurations, I’m wondering if we should invest in automated testing.
We have about 15 different risk matrix configurations across various departments (product quality, supplier risk, process risk, etc.), each with different severity/probability combinations and custom calculation logic. Manual regression testing after each release takes our QA team nearly a week.
What approaches have others found effective for risk matrix testing? Is data-driven test automation worth the investment for this type of functionality, or does the variability in risk scenarios make exploratory testing more practical? Looking for real-world experiences with balancing regression coverage against the effort of maintaining automated test suites.
We went through this exact evaluation last year. For risk matrix testing, we ended up with a hybrid approach - automated regression tests for the core calculation logic and scoring rules, combined with manual exploratory testing for edge cases and user workflow validation. The automation catches breaking changes in matrix configuration, while manual testing handles the nuanced scenarios that are hard to script. Saved us about 60% of our regression testing time.
After working with multiple organizations on TrackWise risk management testing, here’s my perspective on balancing automated and manual approaches:
Risk Matrix Configuration Testing:
This is where automation shines. Risk matrix configurations are essentially lookup tables with calculation rules - perfect candidates for data-driven testing. Build a test framework that validates:
- Severity score calculations across all defined levels
- Probability score calculations and thresholds
- Matrix cell determination (severity + probability = risk level)
- Custom scoring algorithm outputs
- Configuration changes don’t break existing assessments
The key is maintaining a comprehensive test data set that covers all matrix configurations and edge cases. Update this data set whenever business rules change, and your automated regression suite stays relevant.
Data-Driven Test Automation Value:
For 15 different matrix configurations, automation is absolutely worth the investment. Here’s why:
- Initial setup: 2-4 weeks to build framework and test data
- Regression execution: Hours instead of days
- Consistency: Same test coverage every time, no human error
- Early detection: Catches configuration breaks immediately
- Documentation: Test data serves as living documentation of risk rules
The ROI becomes positive after about 3-4 regression cycles. With TrackWise releases and your custom updates, you’ll likely hit that within 6-9 months.
Regression vs Exploratory Testing Balance:
This is where the hybrid approach is critical. Use automation for regression (“does it still work as designed?”) and manual exploratory testing for validation (“does it work as users need it to?”).
Automated regression should cover:
- All matrix configurations and scoring calculations
- Standard risk assessment workflows
- Data integrity (scores persist correctly, history is maintained)
- Integration points (if risk scores feed into other modules)
- Performance (can the system handle bulk risk assessments)
Manual exploratory testing should focus on:
- User workflow effectiveness (is the risk assessment process intuitive?)
- Edge cases and unusual scenarios that emerge from real use
- Cross-functional workflows (risk assessments triggering CAPAs, change controls)
- Usability and user experience issues
- Business rule interpretation in ambiguous situations
For your specific situation with custom scoring algorithms, I’d recommend:
- Automate the core matrix configuration testing (all 15 configs)
- Automate standard calculation scenarios for each algorithm
- Manual test complex algorithm edge cases and external data dependencies
- Manual exploratory testing for new features or significant config changes
- Maintain a regression test suite that runs automatically with each deployment
This approach should reduce your week-long regression cycle to 1-2 days (few hours automated + 1 day manual validation), while maintaining comprehensive coverage and catching both technical breaks and usability issues.
I’d recommend a risk-based approach to your testing strategy - which is fitting given you’re testing risk management! Prioritize automation for your most frequently used risk matrices and those with the highest business impact. For matrices that are rarely used or have highly variable configurations, stick with manual testing. Also consider your team’s skill set - if you don’t have strong automation engineers, the ROI on building a complex data-driven framework might not be worth it.
Data-driven automation is definitely worth it for risk matrices if you have multiple configurations. We built a test framework that reads matrix configurations from CSV files and validates the scoring output against expected results. The initial setup took about three weeks, but now we can run full regression across all 20+ matrix configs in under an hour. The key is maintaining your test data sets - you need to update them whenever business rules change.
From a risk management perspective, I’d argue that exploratory testing is still critical even with automation. Risk assessments involve a lot of contextual judgment that automated tests might miss. We use automation for the mathematical calculations and matrix lookups, but our risk analysts do exploratory testing on the actual risk scenarios to ensure the system supports their decision-making process. It’s not just about validating calculations - it’s about validating that the tool supports the risk assessment workflow effectively.