In designing test cases for research software, it can be useful to conceptually differentiate between tests that verify the technical correctness of the code and tests that check the scientific validity of the results. With technical software tests, you check whether a function behaves as expected. With a scientific test, you compare the outcome of a function to known (experimental) scientific results.
The following questions can help you decide what to test in your software:
- How can I ensure the algorithms and mathematical models implemented in the software are correct?
- How can I verify that the input data types, formats, and ranges adhere to expected standards and constraints?
- How does the software behave at boundary conditions and extreme values of input parameters?
- How does the software perform under varying workloads and dataset sizes, and is it scalable for large-scale simulations or data processing tasks?
- How do I compare the software’s results against existing literature, experimental data, or previous simulations to check their accuracy?
Roadmap to testing
Begin by learning a testing framework that is well-suited for your programming language; for example, you might explore pytest if you are using Python. It is also important to understand the basic types of tests, focusing primarily on unit tests and simple integration tests.
Take the time to inspect your codebase and determine which parts are most important or particularly prone to error. These are the areas where you should focus your testing efforts.
Begin by writing minimal (unit) tests for individual functions or modules. Creating tests based on expected inputs and outputs will help confirm that your code behaves as intended. The primary goal at this stage is to understand the testing process rather than aiming for complete coverage from the start.
As you introduce new functionality, adopt an approach by writing tests alongside your new code. Over time, gradually add tests to your existing code – especially when you make changes or improvements – to steadily increase overall test coverage and improve the reliability of your software. Make sure to focus on testing the critical parts of your codebase first.
If you find it difficult to write tests for your codebase, consider refactoring it into smaller, more testable units. Clear documentation and comments on your functions will further aid in writing tests by providing a well-defined understanding of each component’s intent.
Finally, add automated testing to your development workflow by using a continuous integration tool to run tests automatically with each code change. Establishing a regular habit of testing will, over time, lead to significant improvements in the quality and reliability of your research code.
For researchers already experienced with testing, it is useful to develop a testing strategy. Start by adopting the test pyramid approach: ensure that all core functions and algorithms are covered by unit tests, verify that modules interact correctly with integration tests, and, when applicable, use end-to-end tests to simulate real-world user workflows. Regularly running regression tests is also important to catch any unintended side effects of code changes.
Aim for comprehensive test coverage to ensure that critical parts of your codebase are thoroughly tested. A good benchmark is to test at least 70% of your code base with unit tests.
Consider methodologies such as Test-Driven Development (TDD) into your workflow. With TDD, you write tests before you write the actual code to define the desired behavior, ensure clear specifications, and obtain immediate feedback.
In a research context, certain quality measures become particularly important. Prioritize reproducibility by writing tests that verify experiments yield consistent outputs for a given dataset and configuration. If your code relies on statistical methods, use fixed random seeds to ensure reproducibility across different runs. Additionally, consider implementing validation tests that compare your results against known benchmarks or experimental data.
- Coverage Analysis:
Employ code coverage tools to identify untested paths and critical areas that require additional testing.
- Parameterization:
Run tests with a range of inputs to validate robustness across different scenarios.
- Error Handling: Verify that your code behaves as expected when encountering errors.
- Fixtures: Use fixtures to set up a consistent and reusable testing environment.
- Mocking:
Use mocks to simulate external systems or heavy computations, isolating tests for faster feedback.
- Tagging and Filtering: Organize tests into categories and run specific subsets based on tags or filters. Advanced testing practices
Finally, integrate your testing processes into a continuous integration/deployment pipeline. Automate your test runs so that every code commit triggers a suite of tests to catch issues early in the development cycle. Monitor test performance and code coverage over time to continuously refine and enhance your testing strategy, ensuring a high level of quality and reliability in your research software.