How to Use AI and Technical Strategies to Improve QA Test Coverage
Using AI and technical strategies can vastly improve QA test coverage. Learn how AI-enabled autonomous test case creation, along with code coverage analysis, mutation testing, model-based testing, behavior-driven development (BDD), and integrating tests into CI/CD pipelines, can further enhance testing coverage
Every software application requires thorough testing to find defects, ensure feature completion, and meet performance needs. And if you don’t test the code, you can’t know whether it meets the requirements. As a result, every application developer and QA team should aim for comprehensive test coverage to produce reliable, functional, and secure applications.
But doing it by hand? That’s not a great option. Among the reasons: Manually writing test cases takes a lot of time and effort. It’s challenging to identify tricky edge cases. Keeping tests up to date as the code evolves is demanding. Any experienced QA tester can add more examples.
Gen AI-powered test case generation can make a big difference in software testing by automating the creation of quality test cases, speeding up testing, and improving bug detection. The AI tools can ensure thorough coverage quickly and efficiently.
Why Comprehensive Test Coverage Matters
Test coverage determines which parts of the software the test executed – which is a measurement of testing effectiveness. For instance, if you wrote a routine to resolve a failed e-commerce payment, but the QA tests never exercise those functions, there’s no way to know if that part of the application works correctly – which suggests that it probably doesn’t. Unless you get very, very lucky.
If you don’t test the code, you don’t know if it works.
Comprehensive test coverage is crucial for several reasons:
Assuring the code works: Catch and fix bugs early in the development cycle, so users don’t find them.
Maintenance is easier: Confidence in the code allows for easier maintenance and refactoring. While changes can introduce bugs, comprehensive tests will identify these issues promptly, ensuring the code remains reliable.
Meeting compliance requirements: In some industries, high test coverage is a requirement for meeting strict regulatory requirements.
Manual testing is becoming outdated. If you still rely on it, you might struggle to achieve the level of test coverage you need. Among the limitations:
Manual testing is time-consuming: Writing comprehensive manual test cases is labor intensive, which delays the development cycle.
Manual testing is resource-intensive: Humans are expensive and they are not always available when you need them, especially for large or complex projects.
Human error: People make mistakes and they forget things. That can lead to incomplete test cases and missed scenarios.
Difficulty in identifying edge cases: Edge cases are scenarios where the behavior of the code might be different or could potentially break, such as boundary conditions, invalid inputs, and error handling. It isn’t easy for humans to think of every possible way that things can go wrong.
Maintenance overhead: Keeping manual tests up to date with rapidly changing codebases requires ongoing effort.
Manual testing’s limitations highlight the need for more efficient and effective approaches. This is where Gen AI-powered test case generation can make a difference in software quality by automating and enhancing the testing process.
Ways to Use AI to Maximize Test Coverage
Gen AI, with its almost autonomous testing capabilities, is transforming how developers and testers can handle test creation, diagnosis, maintenance, and documentation.
AI helps organizations achieve exceptional test coverage, ensuring their software is robust, reliable, and ready for anything. AI's natural skill for identifying and creating diverse test scenarios, including those tricky edge cases that manual methods often miss, marks a significant leap forward in software testing.
Here are some of the things you can achieve using Functionize’s testGPT, powered by Test Agents.
Test creation: AI-driven test creation improves the efficiency of software testing. By leveraging real-time user interactions and historical test cases, testGPT can automatically generate a massive range of test cases. It doesn’t just handle the usual scenarios; it also digs into tricky, obscure edge cases that are often missed.
Diagnosis: testGPT quickly spots issues by recognizing patterns and anomalies in test results. It can pinpoint exactly where and what the defect is. Critical issues are fixed right away.
Maintenance: AI automatically adjusts test cases to align with application updates. This near-autonomous maintenance keeps tests relevant and effective.
Documentation: AI-powered tools like testGPT streamline the documentation process. They create detailed and accurate documentation for each test case, clearly showing what’s being tested and why.
Improving Test Coverage with AI
Test coverage development encompasses a range of functions and processes, whether it’s created and managed by a computer or by a carbon-based life form.
Comprehensive test scenarios: AI algorithms can analyze application requirements and user interactions to generate extensive test cases. They can analyze an application’s complex behaviors and generate a variety of test scenarios.
Accuracy and adaptability: Assuming that you aren’t starting from scratch on a new project, your development shop probably has plenty of test cases already. Even with existing test coverage libraries, AI can enhance accuracy by detecting hidden patterns and relationships. Plus, because it adapts to the changes in application requirements, it can keep up with “tiny” modifications that get put off… and should not.
Efficiency and speed: Computers may not have empathy, but they’re fast. An AI process can drastically reduce the time and effort required to generate test cases. Testing cycles speed up. Developers can focus on other interesting tasks, such as the creative innovation we prefer to mind-numbing repetition.
Technical Approaches to Improve Test Coverage
Designing for testability takes time and attention. The effort absolutely pays off, because well-designed tests create a positive feedback loop. Programmers gain confidence in their software because they know the system will find flubs that they can fix before the code goes to production. Consider these types of testing as you work out your test coverage plan.
Code coverage analysis: The higher the coverage, the more confident you can be that all of the features in your application are examined, implying fewer bugs in production code. Tools including JaCoCo, Cobertura, and Istanbul pinpoint code that hasn’t been tested, with detailed reports showing which lines, branches, and functions have been covered.
Mutation testing: Mutation testing effectively tests your tests. It assesses test cases’ effectiveness by intentionally introducing bugs – or "mutations" – into the code. The goal is to determine if the existing tests can detect these changes. If a test fails due to a mutation, it indicates that the test suite is robust. That generates confidence that your tests are doing their jobs at finding real defects. Common tools include PIT (Pitest) for Java and Stryker for JavaScript.
Model-based testing: Model-based testing shows how a system should behave and generate test cases based on these models. The premise is that all possible states and transitions in the application are covered systematically. Tools like Spec Explorer and GraphWalker make this process easier.
Behavior-driven development (BDD): A common way to simplify test-case generation is to use Behavior-Driven Development (BDD), which looks at how the application behaves from the user's perspective. BDD can make it easier to understand and validate application requirements. Test cases are created in a clear, Given-When-Then format. This promotes smooth collaboration among developers, testers, and stakeholders. Cucumber is a popular tool for this purpose.
Continuous Integration and Continuous Deployment (CI/CD): CI/CD pipelines integrate testing into the development process. Code cannot be released into production until it has passed an entire test suite, so that changes don’t break an existing system. Tools such as Jenkins, Travis CI, and CircleCI automatically execute tests whenever new code is added to the repository.
With automated tests in the CI/CD pipeline, organizations expect to achieve higher test coverage and maintain software quality throughout the development lifecycle.
AI: It Isn’t Either/Or
AI-powered test case generation improves test coverage by automating the creation of diverse and comprehensive test scenarios. This makes testing more efficient, faster, and accurate, promising better software quality, faster time to market, and – not for nothing – satisfied developers and QA teams.
But you don’t have to do everything all at once. AI is new. It’s fine to combine AI technologies with other testing strategies.
Explore and implement AI-driven tools in your testing processes to ensure robust and reliable software products.
For more information, consult the resources and tools mentioned in this article:
