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In a world where technological advancements have constantly kept redefining how we live, it was only a matter of time before the theorized AI became a reality. Though AI is perceived with mixed feelings by people, with many fearing that the sci-fi movies about AI might be true after all, so far we can safely say that it is still programs and algorithms meant to help us. AI is a buzzword prevalent not just in the technology sector, but also across other fields like automotive, healthcare, finance, and defense because well, technology is used everywhere. AI has already started revolutionizing how software development is carried out. Since software testing is a crucial part of the software development process, we can see its impact here as well.
AI Automation Testing
The process of testing has come a long way from being a merely reactive approach for identifying bugs. It has turned into a full-blown process that involves proactive planning, strategizing, and executing testing to make sure that the product delivered is in ship shape. Testing evolved from manual testing to scripted automated testing, then to agile testing involving codeless tools, and now slowly heading towards autonomous testing with the help of AI. Though we are yet to reach the goal of autonomous testing, with the capabilities that AI has introduced to this whole testing process, we are already on an accelerated path. With the recent addition of generative AI, we can expect exciting developments ahead in the field of quality testing. If you look at automation as the muscle, then AI is the brain, or at least part of it since human intervention is still needed for the creative and cognitive activities involved in testing. Let’s take a look at some ways in which AI is improving test automation.
How is AI improving automation testing?
Test case creation is getting easier
Many QA teams jump onto the automation bandwagon eagerly only to realize that test case creation and maintenance are a huge overhead. Luckily, AI is blessing the QA market with frameworks like testRigor that make writing automated tests as simple as writing manual test cases. These tools require little to no coding, meaning that anyone can now participate in test automation. By analyzing the modified functionality or requirements, AI algorithms can identify potential scenarios and generate test cases that cover the updated features. This assistance helps improve test coverage.
The nightmare of test maintenance is coming to an end
Consider the automation frameworks that require HTML attributes for identifying UI elements. In an ever-changing, agile-driven environment, the application is bound to keep evolving. Even the slightest changes in the UI element identifiers can wreak havoc in a test automation engineer’s life. Once again, AI comes to the rescue. With AI powering the automation testing framework, changes in the UI element locators can be easily managed. Frameworks that support the self-healing of test scripts can automatically analyze the failures, compare them with the expected results, and make necessary adjustments to the test scripts to fix the failures. This self-healing capability ensures that test scripts can adapt to changes in the application without manual intervention.
Better risk profiling
AI can analyze historical test data, defect repositories, and other relevant sources to predict potential areas prone to failures. This enables prioritization of test cases based on the likelihood of uncovering critical defects, guiding testers to concentrate their efforts on high-risk areas.
Realistic test data sets
In data-driven testing and performance testing, there’s a constant need for large amounts of data. Using the client’s database is often not feasible. AI can step in here, generating realistic user data, simulating a multitude of scenarios, and gauging system response times. Furthermore, it can dissect performance metrics, pinpoint bottlenecks or inefficiencies, and furnish insights for optimization.
Reduced costs for the company
Costs can come in the form of test suite maintenance, hiring skilled resources for automation testing, or bugs in the production environment. All these issues can be brought under control with AI under the hood of your test automation framework. For instance, if the automation tool is a no-code tool then the need to hire specialized automation engineers instead of leveraging the in-house manual QAs who have a trove of experience and know the application the best need not arise. If test automation maintenance was a plague, it can definitely be handled with AI’s help.
Visual testing
Historically, UI changes have predominantly required manual testing. However, with the emergence of AI technologies, we’re now equipped with cutting-edge tools to automate UI testing. Techniques like Optical Character Recognition (OCR), object detection, and tracking are now being harnessed for visual testing. We will dig deeper into this in the next section.
AI UI Testing
Aesthetics add greatly to the first impression of a product. Visual defects can affect user experience as well as the credibility of the brand. Validating applications requires a combination of functional or end-to-end testing, visual testing, and cross-platform testing owing to the extent of digitization prevalent. Ensuring this complete digital experience solely via automation is still not a possibility even in organizations with the most sophisticated testing infrastructure. Using AI to test UI helps manage the below mentioned issues.
Common issues automating UI testing
Shorter test cycles leading to lower test coverage
With Agile orchestrating the show, the time to deliver has decreased. This means that there is a time crunch on what can be tested. Quite often QA teams prioritize testing functionality and workflows, overlooking UI testing.
Test script fragility
Even slight changes in the UI can cause test script failures. Small UI modifications like style changes, layout adjustments, or reordering of elements can inadvertently break automation scripts, requiring script modifications and revalidation.
Test maintenance overhead
As the UI evolves, test scripts may require frequent updates to adapt to changes in the application’s functionality or user interface. UI changes can impact the locators, interactions, or verifications in test scripts, leading to script failures. Maintaining and updating test scripts to reflect UI changes can be time-consuming and resource-intensive.
Lack of a good UI automation testing tool
Along with how you do the job, what you use to get the job done matters as well. Using a testing tool that does not help with efficiency can cost you more than you intend to spend.
Difficult to achieve visual correctness through code
One might think that if they write a test using locators of web elements, then that should do the trick. Even the slightest change in alignment, page layout, rendering, element positioning, fonts, and colors, can cause the tests to fail. Trying to assure visual correctness through code is not only a complex endeavor but also not fruitful.
Traditional visual UI testing techniques
Snapshot comparison
Do you remember the ‘spot the differences’ game where two copies of the same image are adjacent to each other with one differing from the other in some small ways? AI does exactly this by comparing pixels from both images to spot the differences. This technique is useful for visual regression testing, where algorithms can automatically detect visual discrepancies caused by UI changes or updates, ensuring that the application’s visual appearance remains consistent.
However, this technique does pose some challenges like highlighting false positives, and difficulty handling dynamic content like a blinking cursor. Moreover, once these differences in pixels are reported, other issues that might require investigation get overlooked.
DOM comparison
The DOM is a tree structure that comprises nodes. In the DOM comparison model, the DOM trees of the test page and the reference or baseline are matched to identify differences. Issues like the color of the font, font style, and differences in other such attributes can be easily identified using this method. However, this method alone is not enough for visual testing as changes in the rendered content may not get caught. For example, if the file name is the same but the image rendered is actually different, then that will be a false positive.
Visual AI for UI testing
The traditional visual testing techniques are prone to errors and are not as effective as one might hope. With visual AI, it is possible to mimic how the human eye perceives visual information without the human tendency of getting bored, or tired. Since AI is a machine, it is not prone to human errors either.
A system using visual AI first needs relevant visual data, such as images or videos to be collected and prepared for analysis. This can involve gathering data from various sources, curating datasets, and ensuring data quality and diversity. The collected visual data is preprocessed before analysis. Preprocessing steps may include resizing, normalization, noise reduction, or other transformations to standardize the data and improve the performance of AI algorithms.
The collected visual data needs to be labeled with relevant information to create a training dataset. Labels provide ground truth information for supervised learning, allowing AI models to learn associations between visual features and the corresponding labels. AI models, such as convolutional neural networks (CNNs), are trained on the labeled dataset. Training involves feeding the labeled data to the model, which adjusts its internal parameters through iterative optimization algorithms. The goal is for the model to learn to accurately classify or analyze visual data based on the provided labels.
Trained models are evaluated on separate test datasets to assess their performance and generalization ability. Evaluation metrics, such as accuracy, precision, recall, or F1 score, are used to measure the model’s effectiveness. Models may undergo optimization techniques like hyperparameter tuning or architecture modifications to improve their performance. Once trained and optimized, the AI model is ready for inference. It can analyze new, unseen visual data and provide predictions, classifications, or insights based on its learned knowledge. In the case of visual AI for testing, it can detect anomalies, identify objects or patterns, perform visual comparisons, or extract relevant information from visual content.
Tips to create a good test automation process using an AI-powered framework
Identify suitable test cases for automation
Irrespective of how hi-tech your automation process is, it is always prudent to do a proper test design before writing test cases. Not all test cases are suitable for automation. Begin by identifying test cases that are repetitive, time-consuming, or critical for regression testing. Prioritize test cases that provide high value and coverage, considering factors like business impact, user scenarios, and customer requirements.
Choose a suitable AI test automation tool
Make sure to pick a tool that checks the boxes for you. It needs to be easy to use, satisfy your requirements, cost-effective, and scalable. You can find AI-driven tools in the market these days. testRigor is one of the promising ones that we will look into a little later.
Factor in UI testing across platforms
Make sure that the tool you choose is also able to handle cross-platform and cross-browser testing. Since everyone prefers to access content on the go, your application should be compatible with different devices. This brings us to the point of having strategies for tackling web UI and mobile UI testing. This can be a part of your list of considerations when choosing an automation tool as well.
Understand the AI capabilities of the tool
This seems basic but wouldn’t hurt to mention it. Do make an informed decision when choosing your testing framework or tool. It would help to understand the capabilities that it has to offer, see it in action, and look at some reviews, all so that you can rest assured knowing that this tool will add value to your testing process.
Ample support provided for the chosen tool
It always helps to have good help content. This could be in the form of customer support, chatbots, active community, and ample documentation on their website.
Employ CI and good reporting
Integrate your UI test automation process with a continuous integration (CI) system to enable automated test execution as part of the build and deployment pipeline. Configure test execution triggers, generate detailed reports and capture screenshots or videos for failed test cases. This promotes early detection of issues and facilitates collaboration among team members.
Cut down on maintenance overhead
When scouting for the automation tool, check that it helps cut down on regular test maintenance. An AI-driven system is most likely to help with this, but do verify reviews and testimonials to make sure that the claims are true.
End-to-end testing with AI
Just as AI is innovating ways to simplify and improve testing techniques like UI testing, unit testing, and API testing, it is also impacting the end-to-end testing process. End-to-end testing can be time-consuming since it interacts with the entire application, not just the front end or the back end. As we know that time is of the essence in agile, we need ways to make the process of creation, execution, and maintenance of these test cases faster and easier to carry out. These tests usually involve collaborating with stakeholders who are closer to customers and also are the ones who are not well-versed in programming. AI helps mitigate these problems and make test automation frameworks work for you in the truest sense.
AI-driven test automation frameworks
AI is being widely adopted in test automation frameworks like testRigor. It smoothly blends the offerings of conventional automation tools with modern technologies like AI to ensure that testers are unburdened from mundane and repetitive tasks. Let’s take a look at what sets this tool apart from its contemporaries.
testRigor offers visual testing support. It leverages AI to identify and even categorize UI content like images, videos, and text within UI elements or images. You can compare screenshots, UI images with baseline images, and identify or click on UI images by recognizing them with the help of reference images. Image recognition technology is also employed to interact with common icons seen on websites. testRigor classifies these images and can use this information to click on similar-looking elements on the screen. For example, you can simply write ‘click on “cart”‘ and the engine will identify the cart element on the screen.
Test suite maintenance is slashed down significantly thanks to testRigor’s AI-based engine. It has the capability to identify dynamic elements like pop-ups and banners and prevents hindrances in test execution due to these. When trying to reference UI elements in the test cases, there is no need to mention any HTML attributes. You can simply state the relative position of the elements. The engine figures out the element in question during run time.
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You can see that these tests are as straightforward as writing manual tests in plain English. In this example, there’s no mention of UI element locators, only relative positions. So, even if these locators change in the code later on, testRigor can identify this, ensuring that the test remains successful. This approach to test scripting is effective even in situations involving complex UI interactions, such as interactions with table data, email and SMS content verification, 2-factor authentication, file uploads, API testing, and even basic interactions with databases.
You can use testRigor in conjunction with other frameworks, including CI frameworks, test and issue management frameworks, infrastructure-providing frameworks, and databases. Since testRigor supports testing across multiple platforms—web, mobile, and desktop—it offers various browser and device options to expand the scope of testing. However, if you require access to a broader range of devices, you can always employ frameworks like LambdaTest, which integrates seamlessly with testRigor.
Final thoughts
Advancements in AI will lead to better productivity and quality across fields. In its current stage, we can already see how beneficial it is to have AI incorporated into day-to-day activities. Using AI concepts like NLP(natural language processing), neural networks, deep learning, and machine learning, modern testing tools and frameworks are trying to remove hurdles and make test automation a smooth road.
