Technology is progressing faster than ever before. And the emergence of Artificial Intelligence and Machine Learning further improves the standards of technology as we know them today. In fact, AI and ML’s disruptive power is so big that the number of businesses adopting AI grew by 270% in the last few years.
AI and ML also play a pivotal role in enhancing QA testing and QA automation. Integration of AI into Quality Check and Assurance can drive better run-time results than conventional strategies. You can perform software tests such as unit checking, integration testing, system checking, and acceptance checks more quickly and cost-effectively through AI-derived methodologies.
You can also use AI to reduce the test coverage overlap, which can help save resources and time required for test runs. You can even upgrade UI and UX for an application by using AI to test your visual renders.
Now that we know how AI can benefit testing, let’s assess where you can integrate it. You can perform these integrations in-house, or you can take help from a QA expert who has experience with AI-assisted testing.
Here are a few use cases.
1. API checking Use case
By testing APIs through AI tools, you can precisely check the red flags and errors in your services. Instead of just testing APIs, AI can help create API testing scenarios by identifying API calls and discovering patterns and relationships between different components.
There are many tools available for API test execution and reporting.
It’s a free test automation tool designed specifically for API test generation. It runs seamlessly on desktop and mobile. It has an excellent IDE for generating tests regardless of application complexity. You can also create numerous tests and flowcharts through its codeless test writing and recording utility.
It’s an open-source API testing platform with AI-driven test creation and execution. Through this tool, you can generate end-to-end on-demand workflows for compliance and functional testing.
It’s an open-source Java application that can be used for different kinds of tests such as scope tests, load tests, and stress tests. The application is cross-platform, and you can configure it as per project requirements. Joining a Java course online can make you the expert while using open-source Java applications.
2. Creating automated tests.
Another great way to implement AI into QA is by using it to generate tests automatically. Using AI and ML tools, you can create automated tests to analyze different aspects of your application with utmost accuracy.
AI can run timeless checks on the service or product and verify whether they are working efficiently or not. You can also use it for monitoring and reporting. Some examples of AI-assisted Automated tests include:
● Self-Heal Execution of Selenium Tests
Automation tests are great for evaluating software and products, but they can be inconsistent and flaky. To prevent such errors, you can use AI.
Self-healing AI-assisted testing means that the testing software can identify when a particular function has changed. Once it recognizes that, it can change test parameters to suit that particular instance, ensuring that the test doesn’t fail. Self-heal tests integrate technical components and specific weight distribution algorithms to improve reliability.
Self-Heal Execution of Selenium Tests improves software precision by identifying bugs and other inconsistencies in the service. It also reduces application development time. Integrating it in your testing results in a more reliable and refined mode of execution.
● Automating Unit Test Generation and Parameterization
Unit test generation is a very important part of software testing. It targets specific components of the code and tests them separately. It ensures that each part of the code script is checked and there is no room for errors.
By using AI for Unit testing, you can create more precise testing instances. AI automatically creates mocks and stubs to better identify the code variables. You can use these mock and stubs instances to create better, isolated tests and generate precise results quickly.
Parameterization of automated tests through AI means that when a test is conducted repeatedly by altering one parameter, it can create mocks based on only the changed parameters in that particular execution. AI makes sure that all code variables are checked, and optimum code coverage is achieved.
● Visual validation automation testing
Visual validation is a fundamental component of the software testing process. This test considers UI and UX parameters and generates visual tests for a particular application.
Through AI-assisted automation, software visual validation testing tools can more clearly assess all UI/UX defects and bugs in an application. They can evaluate the shape, size, positioning, and color of the application’s objects and parameters. They are more effective at recognizing bugs than the human eye and can check the application’s actual visual correctness without tester intervention.
Using AI to complement your QA efforts can be a great addition to your software testing methodology. AI allows you to create an error-free and cost-effective final result. AI’s autonomous solutions can help remove manual errors, reduce test overlaps, and improve productivity and reliability while reducing the time you need to thoroughly test your products.