Software testing is one of those industries that is continuously evolving. The thing is that there is a constant need for faster, more efficient, and comprehensive software testing. So it is no wonder that the QA industry has undergone numerous changes over the past few decades.
Currently, we live in curious times when manual testing still has its value whereas automation testing, as well as continuous testing, is on the scene as well. Moreover, efforts are being made to introduce artificial intelligence in software testing in order to achieve even better results. All in all, the future of the QA industry is closely linked with AI which has given rise to such a phenomenon as AI-based software testing. So let's consider what it is and its perspectives in detail.
AI in Software Testing and Quality Assurance
To get a clear understanding of the subject, we offer to start from the basics.
Artificial intelligence can be defined as an area of computer science focused on enabling systems to complete tasks that normally require human intelligence.
To imitate human behavior, AI systems process vast amounts of data for training, analyze it to reveal dependencies and patterns that will be used for making predictions about future states. What is important is that such systems constantly fine-tune their algorithms to provide as accurate results as possible.
The main components of artificial intelligence are Machine Learning, neural networks, deep learning, and Natural Language Processing.
As for software testing, it is an essential part of software development that suggests software exploration and trying out in the most diverse ways to ensure its high quality and correspondence with project requirements.
Consequently, AI in software testing is the leverage of AI algorithms in automation testing tools to optimize the QA process and make it as autonomous and efficient as possible.
What is AI-based Software Testing?
AI-based software testing is a buzzword in a QA community so let's figure out what it is.
AI-based software testing is a testing technique implying the use of AI-powered solutions to test a software product effectively. The main purpose of AI-based testing is to make the QA process smarter, quicker, and more effective. Such abilities of AI as data processing, self-learning, decision-making, reasoning, predicting, and others while being properly implied can be extremely beneficial for software testing and open new horizons by turning software testing into a completely autonomous process.
Currently, AI-based software testing is on its way to maturity. Certain attempts have been already made to embed basic AI algorithms into some testing tools that streamline certain facets of software testing. However, the main breakthroughs in this realm are still in the future.
How is AI improving the Software Testing and Quality Assurance process?
As it was mentioned before, currently practices of AI-based testing are in their early days and require further development and improvement. Still, certain advances have been already made and many projects are indeed promising. So let's see in which way AI can be beneficial for software testing.
Accurate testing results
Taking into account all abilities of artificial intelligence we can state with confidence that software testing executed by AI is more accurate than one executed by a human tester who is always exposed to stress, tiredness, overloads, boredom, and other factors that can lead to mistakes and negligence. AI-powered tools in these terms are more efficient as they can perform testing whenever needed with high accuracy.
Self-healed Selenium tests
Test flakiness is one of the major challenges of automation testing. AI can help to overcome it. For example, quite a few AI-driven tools for Selenium test self-healing have been brought to the market.
AI algorithms create appropriate locator strategies to let QA teams save time, get rid of maintenance hassles, and provide test stability. In case one of the locators is broken or missing the self-healing technology matches another most appropriate locator and heals a test on the fly during a test run, letting a tester know about the way the test has been modified.
Complete traceability
Integrated AI-driven analytics collects test outcomes and associates your tests with project requirements and code coverage to provide complete test traceability.
Efficient visual validation
The ability of AI to recognize images has found an active use in automated visual validation tools that check whether UI elements have the correct color, position, size, etc., and detect the bugs that usually are missed out on during manual testing.
Such tools also address the pain of ongoing changes in UI along with a constantly growing number of platforms and devices with different screen sizes, and configurations.
All in all, AI has made UI testing faster and more stable.
Selective testing
AI-powered tools are able to optimize software testing by executing only those tests that are required for checking code changes in a new build. In such a way there is no need to execute thousands of time-incentive tests but only the required ones to provide fast feedback.
Better test coverage
AI-powered automation testing tools execute many more tests of the most diverse types within a short time frame. In such a way QA teams can observe higher test coverage while product quality gets much better as well.
Test data generation
QA experts know that test data plays an important role in the effective software testing process. Therefore, it is important to take up time for such data creation/collection. Thanks to AI this process has become much easier as long as test data can be generated automatically on the basis of production data analysis. As a consequence, AI-generated test data is indeed similar to the real one which makes testing more accurate.
Reduced time to market
As long as AI considerably accelerates test execution, test maintenance, test data generation, and many other aspects of QA procedure, it contributes significantly to time to market reduction which is of high value for a business.
The best AI tools for Software Testing
Considering the power of AI, there are numerous attempts to use it in software testing by implementing it in various automation testing tools. These days there are a plethora of AI-driven testing tools on the market. Some of them have turned out to be especially efficient and got popular at once. It is worth noticing that most AI-based testing tools are commercial and even if they have a free version it offers a limited number of options compared to the premium one. You can find a brief overview of the most commonly used AI testing tools below.
Applitools
Applitools is an AI-driven visual UI testing tool. It automatically checks the look and feel of applications and sites and smoothly integrates with existing tests. So there is no need to bother with the creation of new tests or learning a new test automation language. The entire screen of an application can be validated with a single code snippet. A check of visual perfection across all browsers and screens is just the work of a moment with Applitools.
Testim
Testim is an automated functional testing tool, utilizing AI and ML to accelerate test design, test execution, and test maintenance. In essence, Testim positions itself not as a tool, but rather as a platform because it also offers a test execution environment.
In terms of support, the tool can be run on various browsers and platforms.
Tests are drawn up either via a recorder, code, or both. The tests get stabilized and self-maintained over time as Testim utilizes historical data to rank locators for each UI element individually.
Testim also provides visual validations for pixels, fonts, texts, etc.
Test.AI
Test.ai serves as an Appium element finding plugin, allowing Appium users to use AI-based search strategies for the identification of UI elements. But the core goal of this AI-driven testing tool is to enable code-free test automation with an autonomous selection of elements and execution of tests. Testim uses special bots to inspect the functionality of an application under testing and perform regression testing with the capture of screenshots and performance metrics.
Mabl
Mabl focuses on regression testing. The embedded AI is trained by capturing user interactions and intents. With regard to these recordings, information on the previous test runs, and knowledge about other applications, the tool makes attempts to replicate a user input. Mabl also crawls for broken links, JS errors, visual modifications, and changes in time of page loading/test execution. The test accuracy is also improving with each test run since the tool learns how to differentiate in the best way dynamic and static regions for visual testing.
Eggplant AI
Eggplant AI is one more automation testing tool utilizing artificial intelligence and machine learning for auto-generation of tests, optimization of test execution, bug detection, and comprehensive test coverage.
To ensure a great end-user experience, Eggplant AI provides testing from the end-user perspective which includes app usability, functionality, and performance across the most diverse browsers, OSs, and devices.
The additional features of AI-driven testing provided by Eggplant AI are regression packs, bug hunting, and coverage analysis.
The future of AI-based Software Testing & Quality Assurance
AI-based software testing is in its early days, that is why its full potential hasn't been fulfilled yet. There is a range of obstacles restraining the adoption of AI in software testing. Still, it is AI that shapes the future of Quality Assurance. The current works in this realm are indeed promising so there is a chance that in the near future totally autonomous software testing will become a reality.
More details on the prospects of Artificial Intelligence in the QA field you can find in our article What are the Advantages of AI in Testing.
