Artificial intelligence is one of the latest technologies that’s making its impact across all domains and industries. AI continues to replace human skills and provide significant dominant results. In the field of software quality assurance, AI is making the testing process flexible and easier, thus delivering quality outcomes. Artificial intelligence testing is a technique that uses AI and machine learning algorithms to test a software product.
AI testing makes the QA process highly effective by applying problem-solving and logical reasoning methods to improve the testing process. Also, AI testing tools execute the tests by using the data and algorithms to perform tests without any human assistance.
Artificial Intelligence Testing Tools Categories
Over the last two decades, the paradigm of software QA has significantly evolved. From manual testing to automation testing, the journey of quality assurance has been quite encouraging. Enterprises are also implementing tools that use ML and AI algorithms for software testing. AI testing tools speed up continuous testing, enable automation without human intervention, and improve return on investment.
Let’s look into 4 main categories of AI testing tools that QA companies utilize these days:
Firstly, these tools use machine learning and AI algorithms to identify security vulnerabilities, coding issues through unit test automation, and code scanning. Secondly, some of the tools under the differential tools category are:
Fuzz testing tools aim to make open-source software more stable, reliable, and secure. It combines modern fuzzing techniques with distributed and scalable execution, and supports Rust, Python, C/C++, and Go code.
Firstly, it is a machine-learning algorithm-based tool that predicts the failure for each test case based on past runs and the changes that occurred in the source code during the test. Secondly, the user can record the test suite so that the tests that might fail can run first. By doing so, one can reduce a long-running test suite to a few minutes.
Visual-Based AI Testing Tools
The user interfaces experience changes from time to time. With the growing number of devices and platforms having different configurations and screen sizes, it has become challenging for test engineers to effectively test the UI layer. The visual AI testing tools effectively test all variations of UI layers. Some of the tools that fall under the visual-based AI testing tools category are:
Percy by BrowserStack
Firstly, it is a visual review tool that has features such as responsive diffs, snapshot stabilization, and pixel-by-pixel diffs. Secondly, Percy allows high-speed rendering and cross-browser rendering. Teams can automate visual testing by using this tool. It improves the visual coverage area and allows teams to deploy code changes.
Firstly, it is a next-generation visual AI-powered test automation tool that helps in increasing test coverage and reducing maintenance costs. Secondly, Applitools facilitates cross-device and cross-browser testing and speeds up visual and functional testing. It is easy to integrate with modern test frameworks. Applitools also works with existing tools like Cypress, Selenium, and Appium.
The issues of reliability, maintenance, and flakiness problems persist in automation tests. Because of this, AI and ML algorithms-based self-healing tools were introduced in test automation. These tools are commonly based on a record and playback mechanism where the machine learning engine is embedded in the self-healing of the recorded scripts. The tools that fall under the self-healing tools category are:
Firstly, it uses AI and Ml algorithms to fully automate the testing process. Artificial intelligence speeds up the authoring, maintenance, and execution of the tests. Secondly, Testim facilitates the self-maintenance of the ML-based automated tests. It speeds up the authoring of stable automated tests.
Firstly, it is one of the leading intelligent test automation tools that facilitate continuous integration and continuous delivery. Secondly, Mabl crawls the app screens and runs default tests that are common among mobile applications. It uses machine learning algorithms for defect detection and improves test execution.
- Declarative Tools
These tools improve test automation stability and productivity. Declarative tools use AI and ML techniques and support robotic process automation, autonomous testing methods, natural language processing, and model-based test automation. They also eliminate repetitive, tedious, and error-prone tasks via smart automation. The tools that fall under the declarative category are:
- UiPath Test Suite
Firstly, this tool automates and centralizes the testing process. The UiPath test suite ensures high quality in every automation. Secondly, this tool automates test cases in UiPath studio pro with drag and drop interfaces, manages tests with UiPath test manager, and executes tests with UiPath Orchestrator. It is helping software development businesses with 360-degree testing and allowing testing engineers with better QA testing methodologies.
Firstly, it is a next-generation, AI-driven testing tool that allows agile and DevOps teams to easily achieve test automation goals. Secondly, Tricentis allows end-to-end automation testing of software and mobile applications. It combines multiple aspects of QA testing such as test case design, test data design and generation, analytics, and test automation to enable smooth testing of APIs and GUIs.
As technology is improving, businesses are looking for more agile and DevOps practices. But these technologies require robust tools to support continuous testing and release. QASource uses AI in software testing to help businesses achieve fast test results and reliable products. To know more about AI-based testing tools and practices, contact QASource now.