The use of artificial intelligence in test automation is one of the latest trends dominating the quality assurance industry. In fact, in Capgemini’s survey titled World Quality Report 2020-2021, 21% of IT leaders said that they are implementing AI in some form or another in their testing methodologies, while only 2% said that AI has no part in their future planning.
With that being said, it's easy to conclude that AI will have a significant impact on test automation in the coming years. Since faster product releases and great customer experiences define a business’s success, it has become imperative for companies to test their software applications before going to market. By implementing AI in test automation framework, testers can see the following benefits:
The most essential foundations of AI that are applied to software testing include neural networks and machine learning. When used in conjunction, or individually, these subtypes of AI can help the software testing process in the following ways:
AI impacting software testing involves two-steps. The first is training the system and the second is the implementation of the tests. The AI apps that drive all of today’s “smart” products begin life as observers. They are fed the past actions of human engineers and taught to distinguish optimal outcomes from poor ones based on this experience. They acquire human judgement overtime by mimicking the right actions and dismissing the wrong ones.
The result is a QA tool with an impressive list of attributes. It can:
The ability to adapt and respond intelligently to change is a major benefit to automated testing. Having confidence that your app won’t break due to the movement or development of UI elements, or that you can process thousands of regression tests in minutes across platforms, operating systems and browsers, ultimately saves both time and resources.
AI in automation testing can go a long way to improve the efficiency of testing teams. Creating an abundance of new test cases can cause an overload on the system, which results in delays in retrieving actionable insights from test results, thereby slowing down product launches and updates. AI and automation testing can work well together to deliver the following benefits:
These AI testing tools can be used right out of the box, or adapted to a team’s specific environment. To get the best results, however, you should put them in the hands of the QA experts - the original creators and teachers.
The tools listed below are some of the most effective at leveraging AI to scale QA efforts. Each has its own advantages and drawbacks, and, like any tool, you get better results when you place them in the hands of skilled engineers.
The beneficiary of a recent $16 million series-A investment, Functionize is a cloud-based AI testing tool. It uses natural language processing for test creation and is commonly used for API and UI testing. It operates across Chrome, Safari, Firefox and Edge browsers and across a range of OS, including Android and iOS.
Mabl is another AI startup to have won investor confidence, $20 million this time, and is also a cloud-based testing tool that specializes in all things web. It makes functional testing easier by using machine learning to scour the UI for Javascript errors, broken links and, of course, bugs.
Appvance brings the promise of automation testing without scripting or coding. It tests key functionalities and validations within Javascript web and mobile web after automatically detecting how an app works and sourcing its libraries. It integrates with popular test workflows including Jenkins, TeamCity, Git, Jira and more.
This AI-powered test tool is a mobile app specialist with the same “no need to code or maintain” promise as others on this list. The test bot is able to independently explore an app and generate its own test input to analyze functionality in much the same way as a human engineer would when conducting UI testing.
Used primarily for performance regression testing, ReTest removes the need for your testers to have any programming skills. As with most of those above, the tool automatically scours and tests an app, performing simple “before and after” element comparison.
Testim was created to make automation testing accessible across your product team. It is used for the creation, execution and maintenance of test cases using natural, intuitive language across functional, end-to-end and UI testing. Its dynamic rather than static locator enables testing to run continually even if element attributes are changed.
Designed for visual UI regression testing on web and mobile, Applitools is an AI attempt at removing the need for time and resource-consuming manual UI testing. Being a regression tool, its primary purpose is to confirm that user-rated screens and pages have not changed between tests. It has been developed for a range of SDKs, including the ever-popular Selenium.
All of the AI testing tools we have explored attempt to expand the effective range of automation testing. As with our own automation engine, they can test scenarios with multiple combinations of data at speeds far greater than are humanly possible. When deployed correctly by a QA expert, they can make your team more agile and better able to respond to critical errors quickly.
QASource is uniquely placed to guide you through the possibilities of AI testing tools. Our engineers conduct their own research into the application of computer learning and next-generation algorithms that can make practical improvements to your test coverage and quality. Let our experts guide you through what is possible with a free quote, or call +1.925.271.5555 today.