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:
- Faster release cycle times
- Smooth transition from manual test execution to automated execution
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:
- Discover actionable insights while interacting with system under test (SUT)
- Classify the outcomes of the testing exercise as likely defects
- Calculate the likelihood of an outcome translating into a defect
- Associate events and activities with the outcomes
Role of AI in Software Testing
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:
- Develop an automation framework
- Create page libraries
- Generate object repositories
- Automate UI verification scripts
- Automatically adapt to changes in application UI
- Generate thousands of lines of code in a minute
- Integrate with CI/CD and Git source code repositories
- Produce intuitive execution status reports
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.
Benefits of AI Testing Tools
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:
- Automate Test Case Writing: Instead of running a large test suite to detect a small bug, AI offers precise test cases that are easy to handle and run. With AI automation, testers run a minimal number of tests to determine the impact of change of code.
- Automate API Test Generation: API evaluation helps to measure the quality of interactions between programs that communicate between databases and servers. By using AI, testers can analyze the functionality of connected applications, detect potential risk areas and create test cases.
- Predictive Analysis: AI can use existing customer data to predict how user behavior will evolve. This helps software developers create better products and customer experience to retain clients as well as onboard new ones.
- Identify Errors in Selenium Tests: Although Selenium is one of the best test automation frameworks, it is a complicated time-consuming process, wherein, the smallest error can offset all test progress. AI identifies these broken aspects and repairs them. For example, if a technical error delayed or stopped the test process, then AI would identify the problem and fix it. AI helps to heal Selenium tests automatically, and provides smart insights on improving the case.
AI Test Automation Tools
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.
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Functionize
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.
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Mabl
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.
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Appvance.ai
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.
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Test.ai
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.
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ReTest
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.
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Testim
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.
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Applitools
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.
