Effective User Acceptance Testing (“UAT”) begins with well-prepared test scripts. Test scripts provide a structured framework for validating that a model, program, or template performs as intended—both in terms of functionality and calculation accuracy. Without them, it’s difficult to ensure thorough, consistent testing or to trace and address issues systematically.
A comprehensive UAT should cover two key areas:
- Functionality Testing – Verifies that specific actions or features can be executed correctly. For instance, checking whether a template successfully sends an email to users.
- Calculation Testing – Ensures that outputs are accurate and based on correct inputs and logic. For example, a system may successfully send emails (functionality), but if it sends them to the wrong recipients due to an error in data referencing, it fails the calculation test.
To strengthen coverage, UAT test scripts should include both:
- Positive Testing – To confirm that the solution produces expected results under correct inputs.
- Negative Testing – To check how the solution handles invalid inputs or errors. A well-designed model should flag or halt processing when it encounters critical issues, rather than proceeding with misleading outputs.
Fields Included in Test Scripts
Each test script typically includes the following fields:
- Test Scenario – A brief description of the action or condition being tested.
- Expected Results – The correct output or behavior based on the scenario.
- Observed Results – The actual output produced by the model or system.
- Status (Pass/Fail) – Based on a comparison of expected vs. observed results.
- Remarks/Explanation – Notes on discrepancies, assumptions, or additional observations.
Well-documented test scripts not only support a successful UAT exercise but also become valuable references for future maintenance, audits, and system enhancements.
Specifying Expected Results in Test Scripts
Clearly defined expected results are a critical part of any effective UAT process. Ambiguous or overly general expectations make it difficult to validate whether the model, program, or process is working correctly. To avoid this, the expected results should be as specific as possible, down to the exact figures or system behaviors anticipated for each test case.
- When defining expected results, it’s important to identify the specific fields that will be used for comparison against observed results. In actuarial or financial processing, this often includes fields like reinsurance premiums, reinsurance recoveries, and reinsurance commissions. Highlighting these key metrics allows testers to focus on what matters most, ensuring both the functional and financial accuracy of the outputs.
- Each test case should include its own set of expected results, not just general guidelines. This case-by-case specification makes it much easier for reviewers to spot discrepancies, conduct structured comparisons, and draw clear conclusions about whether the test has passed or failed. Ultimately, well-documented expected results support a smoother UAT review process and contribute to stronger stakeholder confidence in the final solution.
Other topics related to UAT: