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Test Automation with AI

What’s AI?

Frequently referred to by its acronym, AI, Artificial Intelligence is a pervasive term in our contemporary world. It’s integrated into devices and applications we interact with daily. Information Technology (IT) has dramatically transformed by incorporating AI, enhancing capabilities and efficiency. AI has driven innovation and improvements from various tools to search engines and other applications. So, what precisely is AI?

AI refers to developing and applying computer systems capable of performing tasks that typically require human intelligence. These tasks may include interpreting natural language, recognizing patterns, learning from experience, and making informed decisions. AI technology can range from systems that carry out specific, narrow tasks, such as voice recognition, to hypothetical systems that possess general intelligence comparable to human cognitive abilities.

AI can be classified into three types:

  • Narrow AI – Known as weak AI, refers to artificial intelligence systems that are designed and trained for a specific task. They are highly specialized systems and perform very well at their intended tasks, but they don’t possess understanding or consciousness. Examples include most of the AI used today, such as Google’s search algorithms, image recognition software, recommendation systems, and voice assistants like Amazon’s Alexa or Apple’s Siri.
  • General AI – Often termed Strong AI, these theoretical systems can perform any intellectual task like a human, understanding, learning, and adapting across various domains. Unlike Narrow AI, they can transfer learning across studies, demonstrating more extensive comprehension. However, such AI currently remains in the realm of theory.
  • Superintelligent AI – Known as Superintelligent AI, this speculative and futuristic concept refers to an AI that could exceed human intelligence, outperforming us in virtually all economically valuable tasks. Despite its potential benefits, it also raises significant risks and is a hot topic of discussion in the AI community.

Essentially, AI technology embodies the endeavor to create machines that can adapt to and learn from their environment, thereby augmenting tasks currently performed by humans. AI technologies are increasingly prevalent in our everyday lives, often working behind the scenes in systems and services we interact with daily. AI impacts many aspects of modern living, from personalized web search results and recommendations to speech recognition in mobile devices and customer service chatbots.

Why is AI Needed in Test Automation?

The evolution of software testing over the past few decades has been a fast-paced journey. It was initiated with manual testing, where human testers performed every operation. Next came the era of automated testing, which introduced scripts to carry out repetitive tasks, reducing human effort and error. This phase transitioned into agile testing, emphasizing constant feedback and iterative development, making the testing process more adaptive and efficient. Then came continuous testing, which involves early and often during the development cycle to ensure any issues are identified and addressed as soon as possible.

Nearly every application integrates some form of Artificial Intelligence (AI). For instance, many applications feature AI-powered chatbots or are designed to work with voice assistants such as Alexa, Google, or Siri. To thoroughly test these AI-enabled features, it’s crucial to incorporate AI within test automation. This ensures that the unique behavior and responses of these AI components are effectively evaluated under different scenarios, contributing to the overall robustness and reliability of the application. Here is an article about why you need test automation.

Consider self-driving cars; for instance, if the vehicle’s intelligence malfunctions or makes an erroneous decision, or if there’s a delay in response time, it could result in a car accident, posing a significant threat to human life. Without the use of AI in test automation, the complexities of modern AI-based features may not be fully addressed, potentially leading to lower quality or performance issues in the final product. As the prevalence of artificial intelligence in our daily lives continues to expand, the necessity for its testing escalates correspondingly.

Read here about the applications of generative AI in testing.

How AI Supports Test Automation?

As we discussed earlier, the necessity of integrating AI with test automation and why we need to do that, let’s see how it will benefit software testing in the long run.

  • Codeless Automation: One of the most significant advantages of using AI in test automation is creating codeless scripts. Using Natural Language Processing (NLP), AI helps to convert test scripts written in plain English to actual code. The benefit of codeless testing is that it gives anyone in your team the power to generate tests, making the whole process more user-friendly and accessible. We will discuss it more later in this article.
  • Automated Test Generation: AI can assist in analyzing software behavior, user interaction patterns, and underlying code, consequently generating test cases autonomously. This feature is particularly advantageous as it shortens the manual effort and time invested in creating test cases while rigorously examining various functionalities.
  • Removing Flaky Test: Testing teams invest substantial time in determining whether a test failure results from application bugs or poorly constructed test cases. These test failures, known as flaky tests, can unnecessarily stall a release, leading to software delivery delays. AI can assist teams in tackling the issue of flaky tests by creating more robust test cases and identifying patterns in random test failures, thereby expediting the process.
  • Increased Test Coverage: AI helps analyze extensive data and identify more testing scenarios that may have been overlooked. As a result, the testing process becomes more comprehensive, and the software quality improves; this enhanced coverage also allows AI to discover and simulate a broader range of test scenarios, leading to more extensive testing and better software quality overall. AI can handle complex test scenarios and high volumes of test data more effectively than traditional testing approaches.
  • Adaptive Test Maintenance: One of the known challenges in test automation is maintaining test scripts when there are changes in the software’s UI or functionalities. Traditional automated tests can fail under these conditions. AI-based testing tools can learn from application changes, whether minor UI adjustments or significant functional updates, and update test cases accordingly. This learning ability minimizes the maintenance effort and increases the resilience and reliability of test cases, keeping them effective even when the software under test evolves.
  • Visual Validation: AI can be utilized in visual testing to compare the visuals of an application against expected results. AI algorithms can compare screenshots of an application’s UI against a baseline image to detect any discrepancies, whether as noticeable as a misaligned button or as subtle as a color shade difference. This is particularly useful for testing across different devices, browsers, and screen resolutions.
  • Enhanced Accuracy: Manual testing invariably risks human error and traditional test automation struggles when faced with dynamic data. By utilizing machine learning, the test comprehends how different elements are expected to operate, thus reducing test authoring time. These features can spare your team countless hours of inspection and correction while concurrently boosting the accuracy and quality of your tests.
  • Reduce Cost: With its ability to perform tasks quickly, reduce manual effort, and minimize errors, AI can significantly reduce the cost of testing in the long run. While there might be an upfront investment in AI-powered testing tools, the return on investment becomes apparent through quicker testing cycles and higher-quality software.

Read here how to save the QA budget.

Continuous Testing with AI

Continuous testing poses significant challenges due to factors like siloed automation, limited visibility of requirements, and the volume of tests. However, by integrating Artificial Intelligence (AI) and Machine Learning (ML) with continuous testing, we can establish an autonomous and zero-touch quality assurance (QA) process. This integration is necessary because software releases occur weekly, with frequent updates rolling out almost every other day, demanding a streamlined and more efficient testing approach.

AI integration enhances testing by enabling QA teams to trigger unattended test cycles, leveraging insights derived from historical data and past events to identify defects. AI-driven algorithms emulate human intelligence, while ML automatically updates test scripts, eliminating unstable test cases. AI-based engines ensure that only robust code progresses to subsequent stages, while ML-specific algorithms analyze data to extract patterns and make predictions. This holistic approach significantly enhances software testing processes. Additionally, LambdaTest stands out as one of the leading AI powered test automation tools, providing advanced capabilities for streamlined testing workflows.

Know more about what is DevTestOps and TestOps in Agile.

AI-based Methods for Test Automation

AI-based methods for test automation encompass a range of techniques and approaches that leverage artificial intelligence to enhance software testing automation. These methods can include:

  • Regression Suite Automation: Regression testing can be time-consuming and labor-intensive for testers. However, with the integration of AI, regression suite automation becomes more intelligent and efficient. By leveraging AI algorithms, automated tests can intelligently determine the necessary test cases to run based on changes in the code. The primary objective of this approach is to reduce the time required to execute a regression test cycle by selecting and implementing the appropriate set of test cases. This targeted and intelligent selection of test cases ensures that the regression testing process is optimized, saving testers time and effort while providing adequate test coverage.
  • Self-healing Automation: AI-enabled self-healing in test automation involves automatically resolving script breakages due to changes in objects or other properties. The key objective of this approach is to minimize the need for manual intervention and expedite the self-healing process. When changes are detected in the application or its elements, AI algorithms can dynamically adapt the test automation scripts to accommodate these modifications. This ensures that the test automation remains robust and functions accurately, even with evolving software or UI changes. By reducing the need for manual intervention, the self-healing process saves time and effort, enabling more efficient and seamless test automation maintenance.
  • Data Analytics: Software testing entails a meticulous and ongoing process involving deploying numerous test cases for each software version to ensure a bug-free release. Consequently, a substantial volume of data is generated, necessitating analysis to interpret test results and provide feedback to the development team. AI tools play a significant role in data categorization and even identifying testing errors, such as false negatives. By leveraging AI algorithms, testers can streamline the analysis process, gain insights from the data, and make informed decisions. This integration of AI in software testing enhances efficiency, accuracy, and the overall effectiveness of the testing process.

Intelligent Test Automation: testRigor

testRigor stands out as a unique codeless automation tool due to its comprehensive integration of AI technology, such as generative AI, in all aspects of the testing process, setting it apart from traditional and other codeless automation tools. testRigor leverages its Artificial Intelligence capabilities across various aspects of the testing process, including test case generation, test script creation, read image from text, and test data generation.

testRigor revolutionizes the test creation process by enabling testers to create test scripts in plain English. This expands the scope of test creation beyond just the QA team to include business analysts, managers, and other stakeholders. Using Natural Language Processing (NLP), a form of Artificial Intelligence, testRigor seamlessly converts English test scripts into executable code, accelerating the test creation process, increasing the test coverage, and simplifying the handling of complex and edge cases. As a result, test creation becomes more accessible, time-saving, and cost-effective.

The primary cause of flaky tests for every automation is the XPath failure. A change in a DOM element makes the XPath fail, creating flaky tests. testRigor handles it differently. It uses AI to capture different properties of the element.

So, the user can just mention either the name or the element’s relative position, and testRigor handles the rest. That means the user can mention Submit or 3rd hello. Let’s see a sample sign-up test script to understand the above two features. Also, you can compare how many lines of code may be needed to write if the same scenario is automated in any other traditional automation tool.
click "Sign up"
generate unique email, then enter into "Email" and save as "generatedEmail"
generate unique name, then enter into "Name" and save as "generatedName"
enter "PasswordSuperSecure" into "Password"
click "Submit" to the right of "Cancel"
check that email to stored value "generatedEmail" was delivered
click "Confirm email"
check that page contains "Email was confirmed"
check that page contains expression "Hello, ${generatedName}"

Using testRigor, we can distinguish different types of content in the application. It can accurately identify texts, images, and images containing texts. This feature helps perform tests based on visual elements and can be used as a part of the element locator strategy. Also, testRigor’s AI can classify images like arrows or dropdowns, etc., helping in accessibility testing. Read here how to build an ADA-compliant app.

testRigor’s AI plays a crucial role in visual regression testing, a process that involves comparing the user interface (UI) of the current version of an application with its previous version. This comparison helps identify any discrepancies or mismatches in the alignment or positioning of UI elements. By leveraging AI algorithms, testRigor can accurately analyze the screen UX and detect even subtle changes that may have occurred between versions.

Also, it minimizes maintenance efforts through its self-healing capabilities; UI or element attribute changes are automatically incorporated into the scripts. There are many other ways the testRigor AI transforms the test process:

  • Website analysis for autonomous test creation
  • Identification of sudden pop-ups and banners
  • Detection of broken pages
  • Building tests based on user interaction analysis

Read more about AI in software testing. testRigor offers a wide range of features, including various testing types and built-in integrations, making it a comprehensive and versatile testing platform. Here are testRigor’s top features.


We are fast approaching a time when even Continuous Testing will need help to keep pace with the shrinking delivery cycle times, increasing technical complexity, and accelerating rates of change. The integration of Artificial Intelligence in test automation, exemplified by platforms like testRigor, is a game-changer in software testing.

This transformative approach improves accessibility, collaboration, and efficiency, producing more robust quality checks for software applications. testRigor’s integrated AI features provide a glimpse into the future of test automation, where advanced technologies work hand in hand with human expertise to deliver high-quality software in an agile and fast-paced environment.

Join the next wave of functional testing now.
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