Focus on cross-functional testers who are skilled in both manual and automated testing. Utilize open-source testing tools and prioritize high-risk areas for testing. Automation can also help reduce the manual effort required, saving costs in the long run.
To make this transition, start by understanding AI-specific concepts like machine learning models, neural networks, and AI validation techniques. Testers will need to learn how to validate AI outputs, look for biases, and adapt to AI’s unpredictability.
The best way to avoid testing your own work is by peer reviews and rotating testers across different teams or projects. Another approach is to implement automated testing suites, which offer unbiased and consistent results without being influenced by the developer or tester.
Mutation score, code coverage, defect detection rate, test execution time, and test stability are reliable metrics. Mutation testing (like with Pitest) is particularly effective because it measures how well your tests can detect changes.
- Mutation score: Measures the quality of test cases.
- Code coverage: Percentage of code covered by tests.
- Defect detection rate: How well tests catch bugs.
- Test execution time: Speed of tests.
- Test stability: Consistency of test results over time.
Manual testing is really good for checking out complex situations, trying out new things, or when you need to rely on your gut feeling or how users will react—things that automated tests might miss or not check out properly.
Absolutely, using failure injection is a smart move, especially when you need to make sure your system can handle failures (like in cloud services). By making things fail on purpose, you can see how well your tests and the system hold up when things get tough.
To stop testers from making fake tests, push them to come up with real-life scenarios that users might face. Keep checking test scenarios often and try out mutation testing to see if the tests are actually useful and working well.
AI-assisted testing and self-healing tests are really cool ideas for the future. As AI gets better, we might see AI come up with test cases on its own, automatically spot weak test cases, and even learn from test results to make better tests in the future.
- Performance Overhead: It’s slow on large codebases.
- Fix: Use parallel processing, and test critical code areas.
- Equivalent Mutants: Irrelevant changes that don’t affect behavior.
- Fix: Filter or manually review these mutants.
- Interpreting Results: High mutation scores can be misleading.
- Fix: Combine with other metrics like code coverage.
- Setup Complexity: It’s tricky to set up for the first time.
- Fix: Start small and provide team training.
- Limited Scope: Doesn’t cover non-functional testing like performance.
- Fix: Use alongside other testing strategies.
Mutation testing can use up a lot of resources, especially for big code bases, and might not always point out important issues with small changes. It also doesn’t catch every kind of bug, and setting up mutation testing can be a big initial work.
The tough parts include the high cost of computing, slow performance on big code bases, and the chance of getting “equivalent mutants,” where changes don’t actually change how the code works, making it hard to make sense of test results.