What is the best way to choose test cases for your algorithm?

1 Test case types

Depending on the input and output specifications, expected behavior, and possible edge cases, there are various types of test cases that can be used to test an algorithm. Normal test cases cover typical and common scenarios, while boundary test cases explore the limits and extremes of input and output. Invalid test cases provide invalid or unexpected input to the algorithm, testing its ability to handle errors and exceptions. Random test cases generate random input to help discover hidden bugs or test the robustness and performance of the algorithm. All of these should be included in a comprehensive testing strategy.

2 Test case selection

To select the best test cases for your algorithm, you should follow some general principles and guidelines. You should use a combination of test case types, such as normal, boundary, invalid and random test cases. Additionally, you should use a representative sample of test cases that is neither too large nor too small. Additionally, you should use a systematic approach to generate test cases based on the input and output specifications, the expected behavior, and the possible edge cases of your algorithm. This can be done via techniques such as equivalence partitioning, boundary value analysis, error guessing, or random testing. Lastly, you should use a consistent template or format to document and organize your test cases. This includes the input, the expected output, the actual output, and the status (pass or fail) of each test case. Comments or descriptions can also be used to explain the purpose or rationale of each test case.

3 Test case execution

Executing the best test cases for your algorithm requires following best practices and tips. For example, use a testing tool or framework that is compatible with the programming language and environment of your algorithm, such as JUnit, PyTest, Mocha, or Google Test. Additionally, a debugging tool or technique can help identify and fix errors or bugs in your algorithm; breakpoints, print statements, log files, or profilers are some examples. Lastly, create a test case report or summary to show the results and status of your test cases; test case tables, charts, graphs, or dashboards are some examples. This report or summary will help you analyze and improve your algorithm and your testing.

4 Test case review

To review the best test cases for your algorithm, you should follow some quality criteria and standards, such as accuracy, completeness, relevance, and clarity. Your test cases should produce the correct output and match the input and output specifications. They should also cover all potential scenarios and edge cases, as well as be relevant and up-to-date. Additionally, your test cases should be clear and understandable to you and others, with well-documented and organized names and formats.

5 Test case improvement

To improve the best test cases for your algorithm, you should follow some feedback and improvement methods such as test case evaluation, feedback, and refinement. Test case evaluation should measure and compare performance and quality of your algorithm and testing using metrics like test coverage, execution time, error rate, or defect density. Test case feedback should be sought from yourself and others using methods like self-review, peer review, code review, or user feedback. Test case refinement should modify and refine test cases based on the evaluation and feedback of your testing. You can use methods like test case prioritization, optimization, maintenance, or reusability to refine your test cases.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?