10 SASUnit Best Practices for Reliable SAS CodeSASUnit is a unit-testing framework for SAS that helps ensure code correctness, maintainability, and repeatability. Applied consistently, unit testing moves SAS development from ad hoc scripting to a disciplined, test-driven approach — especially important in analytics and regulated environments. Below are ten practical best practices to get the most value from SASUnit, with concrete tips and examples.
1. Start with a clear testing strategy
Define what you will test and why. Not every line of code needs a unit test — focus on:
- critical business logic (calculations, thresholds, transformations)
- data validation (schema, required fields, ranges)
- macros that encapsulate reusable logic
A testing strategy should include test scope (unit vs. integration), pass/fail criteria, and how tests fit into release processes.
2. Structure tests to be small, independent, and repeatable
Write tests that verify one behavior at a time. Each test should:
- set up its own input data
- not rely on external state or the order of other tests
- clean up after itself (temporary datasets, macro variables)
Small independent tests make it easier to diagnose failures and enable parallel execution.
3. Use fixtures (setup/teardown) consistently
SASUnit supports setup and teardown routines. Use them to prepare test environments and remove artifacts afterwards:
- create minimal input tables in setup
- restore system options or macro variables in teardown This prevents cross-test contamination and keeps test code concise.
4. Mock external dependencies
When tests depend on databases, web services, or large datasets, mock those dependencies:
- create small representative tables instead of querying production
- use macro-level stubs for external calls Mocking improves speed and reliability, and avoids data privacy issues.
5. Assert on meaningful outcomes, not implementation details
Assertions should focus on behavior and results:
- expected row counts, value distributions, key calculations
- presence/absence of expected variables or flags Avoid asserting on internal variable names or intermediate step counts unless they are part of the contract.
Example assertion ideas:
- dataset A has exactly N rows
- variable X equals the result of a known formula for sample inputs
- no missing values in critical columns
6. Parameterize tests for edge cases and boundary conditions
Create test cases that cover:
- normal cases
- boundary conditions (zero, empty strings, min/max values)
- invalid inputs and expected error handling Parameterization reduces duplicate test code and improves coverage.
7. Keep test data small, explicit, and version controlled
Store test datasets and expected outputs with the code repository:
- small, hand-crafted datasets make failures obvious
- expected outputs (golden datasets) allow quick diffs
- version control test inputs/outputs alongside SAS programs
Avoid heavy reliance on generated or production extracts unless necessary for realistic tests.
8. Integrate SASUnit into CI/CD pipelines
Run SASUnit tests automatically on commits, merges, or scheduled builds:
- fail builds on test failures to prevent regressions
- generate test reports and attach them to build artifacts Continuous testing shortens feedback loops and improves code quality.
9. Monitor and maintain tests — treat them as production code
Tests rot if ignored. Assign ownership and periodically:
- review failing or flaky tests
- remove redundant tests
- update tests when legitimate requirements change Track test coverage trends and prioritize gaps that affect business-critical code.
10. Use clear naming and documentation for tests
Descriptive test names and short comments help future maintainers:
- name tests by behavior (e.g., test_calculation_rounding_for_negative_values)
- document why a test exists and any non-obvious setup Good naming makes test reports meaningful and troubleshooting faster.
Conclusion
Applying these best practices will make SASUnit tests more reliable, faster, and easier to maintain. Focus on small, independent tests that assert meaningful outcomes, mock external systems, version-control test data, and automate execution in CI. Over time, a well-tested SAS codebase reduces production incidents and increases developer confidence.
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