AI and ML are growing exponentially and covering almost all technical domains. According to a report, AI and ML in automation testing have led to a 40% increase in operational efficiency and test productivity across the industry. Artificial intelligence can intelligently execute tasks by mimicking human behaviors and thought processes, while machine learning is a subset of AI. The automation testing field also adopts AI and ML to make its testing and deployment process more convenient and accurate. With the help of these advanced AI and ML tools, the organization makes its testing much more productive with less time and money.
There are many tools and frameworks for performing automation testing, but Selenium is one of the most preferred for website testing. Integrating AI and ML with Selenium for automation testing brings some excellent results. In this article, you will see a brief about AI and ML, and then you will see the role of AI and machine learning in Selenium automation and how they are transforming the automation industry.
Introduction to AI and ML
AI (Artificial Intelligence) and ML (Machine Learning) are the two most closely related terms that many users confuse. It has been seen that these two terms are often used interchangeably. But they are not the same. Although they are set and subset, that is why they are related, but they are not the same. Artificial Intelligence is the field of developing computers and robots capable of behaving in ways that mimic and go beyond human capabilities. Artificial Intelligence is the process where a machine tries to make decisions like a human brain.
Now, talking about machine Learning. Machine learning is teaching computers to identify patterns in data and make predictions or choices based on those patterns. Large volumes of data are generally sent to the computer to find patterns and predict upcoming, unforeseen data. Experts can use each machine learning type to address different problems. ML is classified into four types: supervised, semi-supervised, unsupervised, and reinforcement learning.
How do AI and Machine Learning Transform Automation Testing?
The use of AI and ML in Selenium automation testing is continuously evolving and expanding. Many software organizations are adopting AI and ML to make their testing more efficient at low cost, with less human effort and less time. Let us see the various pointers on how AI and ML are transforming the automation testing field.
- Time
Each time the new project gets started, the team needs to write the tests again by themselves. This becomes a hectic task for the team members and leads to rewriting a significant amount of code, regardless of the reusability of components. The use of AI and ML helps to save rewriting time by offering a solution that enables the rapid and automated generation of test scripts.
- Changes
In the development and testing phase, the teams make several changes according to the stakeholders, users’ feedback, and many such instances. Such alterations can lead to the failure of test scripts when performing certain actions on the page. AI and ML algorithms help in this case by using concepts like “auto-healing” to navigate these modifications and provide an error-free test script execution.
- Test Coverage
In Agile development, it is not a good practice to run the complete regression test suite after every change done in the project. However, this can be handled using AI and ML tools. Many such tools are designed for managing these particular alterations. You can also improve the scope of the Selenium application testing with tools like LambdaTest.
LambdaTest is an AI-powered test orchestration and execution platform for test orchestration and execution that harnesses the power of artificial intelligence to facilitate seamless and efficient testing on a large scale. It offers a versatile testing environment, empowering users to conduct both real-time and automated tests across a wide range of configurations, including over 3000 browsers and real mobile devices.
Here are some key features and aspects of LambdaTest:
- AI-Powered Test Orchestration: LambdaTest intelligently manages and coordinates the execution of both manual and automated tests using artificial intelligence. This ensures that the testing process is optimized for efficiency and accuracy.
- Scalability and Versatility: One of the standout features of LambdaTest is its ability to scale testing operations. Whether it’s real-time manual testing or automated tests across various environments, LambdaTest provides the necessary infrastructure and resources to meet testing needs at any scale.
- Real-Time Testing: With LambdaTest, users can perform real-time testing, allowing them to interact with applications and websites manually. This feature is particularly useful for exploratory testing, debugging, and ensuring the user interface functions as expected in different environments.
- Automated Testing: LambdaTest supports automated testing, enabling users to script and execute test scenarios. This feature significantly speeds up test execution while ensuring reliability, especially for repetitive tasks like regression testing.
Role of AI and Machine Learning in Selenium Automation
Now, let us see the various roles in which AI (Artificial Intelligence) and ML (Machine Learning) are helping the software application industry and transforming automation testing.
- Predictive Analysis
AI and machine learning greatly help automation testing by doing predictive analysis. It uses AI and machine learning algorithms to predict potential problem areas in your web application. AI and machine learning algorithms use past data to predict future possibilities. Predictive analytics is a branch of advanced analytics that predicts future outcomes using historical data combined with statistical modelling, data mining techniques, and machine learning. Many automation testing companies are adopting AI and ML to perform Selenium automation testing as they help to do predictive analysis.
The various methods included in the predictive analysis to transform the Selenium automation testing are logistics, linear regression model, etc. Also, predictive analysis comprises many advanced concepts, such as decision trees, neural networks, etc, to predict the future possibility of errors and bugs based on previous testing data.
- Enhanced Test Coverage
Test coverage is one of the crucial factors that help to make your automation testing process efficient and productive. Artificial intelligence and machine learning models help to make your Selenium automation testing well-organized and more productive by enhancing the testing scope. Achieving effective and comprehensive test coverage is a major challenge in cross-browser testing. Enhancing your test coverage for Selenium automation testing is not easy, especially when using a manual testing approach. So, using AI and ML, you can expand and enhance your testing scope. Test coverage is defined as the configurations, types of devices, and browser-OS combinations for which you can test your mobile app.
It also helps to ensure that the software experience increases and the end user gets the best possible experience while using the application. AI and ML algorithms play an important role in Selenium automation testing by analyzing vast amounts of data and inputs from test runs to identify gaps. This data-driven approach also makes sure that there should be comprehensive test coverage and minimum potential risks.
- Adaptive Continuous Testing
Automated testing should be integrated into your CI/CD pipeline to ensure that tests run automatically after every modification made to your code. Continuous integration and continuous deployment (CI/CD) reduce risk by identifying issues early during development; additionally, they offer faster feedback cycles and quicker timeframes for making changes. AI and ML play a very important role in the CI/CD pipeline when performing Selenium automation testing. With the advanced algorithms of AI and ML, the testing team can prioritize the tests according to the code change, ensuring recent code alteration receives instant validation.
With the help of various artificial intelligence and machine learning algorithms, the team can put immense productive efforts into improving the Selenium automation testing process. The AI and ML algorithms help process the previous data and records to make future test decisions. Whether it should be in the pipeline or not, how beneficial it would be, and many such factors led to improved testing results. In this way, AI and ML are transforming Selenium automation testing with the help of contributions in advanced and effortless continuous testing.
- Visual Validation Testing
Visual validation testing is a prime concern in today’s automation testing era. Visual validation testing in software is a quality assurance activity of verifying if the visual aspects of the application’s user interface seem appropriate to the user. The aim of visual validation testing plays an important role in Selenium automation testing. When you develop a software application or website, you try to make your application more user-friendly with an easy interface and visual components. If not, it may lead the user to opt for other similar websites, which is not in the interest of the software organization.
So, with the help of AI and ML, you make the visual validation much easier and more accurate. AI and ML algorithms can identify visual errors and bugs that cannot be detected manually. AI and ML in Selenium test automation enable comprehensive comparisons of images/screens across different different browsers and types of devices. The system detects minor UI discrepancies, which helps to ensure a consistent user experience across platforms.
- Natural Language Processing
AI and ML also simplify Selenium automation for stakeholders and team members from different languages. As we know, NLP ( Natural Language Processing) is the branch of computer science and, more specifically, the branch of artificial intelligence or AI concerned with giving computers the ability to understand the text and spoken words as human beings can. NLP consists of various trained models that translate text from one language to another.
In Selenium automation, this concept reduces a lot of heavy translation tasks for the testing team. AI and ML tools automatically arrange and translate the language, and hence, the language barrier gets degraded to a large extent. In this way, with the help of NLP, AI and Ml play a very important role in Selenium automation.
Conclusion
With the growth in the testing industry, many automation tools and frameworks are available. However, Selenium is the most trusted tool for website testing. With the integration of AI and ML, Selenium automation has become a much more robust testing concept. AI and ML play a very important role in Selenium automation by saving valuable time, doing predictive analysis to predict potential problem areas in your web application, enhancing the testing scope and coverage, adaptive continuous testing, etc.
