Artificial Intelligence
Automation testing is an important part of the modern application lifecycle. This is because the complex nature of modern apps is impossible to verify without the inclusion of this advanced testing process. During the early years of test automation, the developers could use this process only with the help of Selenium. So, we can say that Selenium had a monopoly in the early stages of the automation testing market. However, in recent times, many new alternatives like Cypress and Playwright have arrived in the market. So, the developers using Selenium automation testing must use other inclusions to improve and justify the use of this test automation suite. Various modern technologies help developers to improve the efficiency of their Selenium test bench.
The inclusion of various advanced technologies like machine learning and artificial intelligence can highly enhance Selenium-based automation test cases. So, this article will focus on the introduction of these practices and how the developers can benefit from them. Finally, we’ll also discuss some of the best tools and platforms to enhance the Selenium-based automation test cases.
Understanding Automation Testing and Selenium
It is straightforward to understand the meaning of test automation by properly analyzing its name. It is the process of eliminating the enforcement of human developers from the application testing process. For this step, the system will use a text file consisting of all the required testing parameters. These parameters will help the system to interact with the application like a normal user. Based on these interactions, the system will generate a report that will show whether the application is ready to be released to the open world. It is worth mentioning that the developers can store the Selenium test cases for reusing them in future app development projects. It is also possible to make changes in the text file to make the results customized according to the requirements of the developers.
However, there is a massive disadvantage to initiating automation testing that all developers and testers must be aware of. This is the initial expense that comes with test automation. All the expenses will mainly cover the needs of integrating various tools, libraries, and plugins that are critical for the proper execution of automated test cases. So we recommend all companies have a definite idea about the business perspectives and possible returns on investment to understand whether they’re ready to make the transition or not.
Selenium is an open-source automated testing suite for initiating test cases on web applications. An interesting fact about the test suite is that its initial name was JavaScriptTestRunner due to its massive dependency on this well-known programming language. Since its initial introduction in 2009, Selenium has gone through four major updates that have massively improved its usefulness and simplicity. A common myth regarding Selenium is that it is a standalone tool. In reality, it is a test suite consisting of multiple different tools that assist the developers at various stages of the application development process. Selenium also has a massive drawback similar to automation testing. This is because, with the help of Selenium, the developers can only initiate automated test cases on web applications. So while testing other apps like native apps, hybrid apps, and cross-platform apps, the developers need to look for an alternative.
The components of the Selenium test suite are as follows:
- The Selenium WebDriver is the most important part of the test suite as it helps the developer not only initiate the test cases but also to redirect them to the respective components of the test suite. With the Selenium WebDriver, the developers can also initiate their test cases in the native environment of the web browser. The WebDriver is an upgraded version of the Selenium Remote Controller as it helps the developers and testers to natively interact with the components of the web application.
- The Selenium Grid is an important part of the suit that helps the developers to improve the efficiency of the test cases. This is because, with the help of the Selenium Grid, the developers can initiate the test cases in a parallel format. With the parallel configuration, it is possible to initiate multiple test configurations on different systems and browsers at the same time.
- The Selenium API is very crucial to improve the simplicity of the test case creation process. This is because, with the Selenium API, the developers can write the automation test cases in their preferred programming languages like Python, Ruby, and JavaScript. After this process, they can import it into the native environment of Selenium. It is interesting that before the inclusion of this tool, the developers had to learn the native Selenese language for this process.
- Finally, the Selenium IDE, also known as the Selenium integrated development environment, helps the developers interactively create test cases in real-time. This is because, with the help of the Selenium IDE, the developers have to use the application like a normal user for creating the test cases. Based on the user interactions, the system will continue to generate test cases. Since the test cases are generated based on real user interactions, they are highly accurate in determining the functioning of the application.
Importance of Machine Learning and Artificial Intelligence in Selenium Automation Testing
Advanced technologies like machine learning and artificial intelligence can massively benefit Selenium-based automation test cases. Based on our research and understanding, we have created a list of specific sectors that can massively benefit from these interactions. They are as follows:
- One of the major challenges in Selenium-based automation testing is to locate the dynamic elements present in a web application. However, with the help of machine language, the developers can easily train the model to automatically locate these elements present on the web application. So this process can help the developers to massively reduce the application testing lifecycle.
- The developers can use artificial intelligence to analyze the test results. With the help of artificial intelligence, it is possible to train the machine to understand past test results and predict certain issues that can arise in the application in the coming years. It is a very important process that can help the developers to avoid unexpected errors or surprises during the application development lifecycle. With the help of this prediction, the developers can also stabilize the application during the updating process.
- Application developers can use artificial intelligence to train the user interface to understand the testing patterns and their impact on the particular elements of the web application. The system can also analyze the frequency of the test cases and optimize the test case execution process. It is a very important process when the developers are working on complex applications and need to deliver the final app in a fixed period.
- Artificial intelligence is an excellent component to create self-healing test cases. By the term self-healing test cases we mean creating such test cases that can automatically detect the errors and sort them out. These test cases are also capable of automatically running the debugging processes on the web applications. The integration of artificial intelligence with the application debugging process is one of the most effective features that will dominate the application development industry in the coming years.
- The use of artificial intelligence and machine learning can help developers to analyze the sentiments of users. This means that with the help of this advanced technology, developers can scout through social media and various user forums. The primary motive of this search is to look for various user feedback and complaints regarding the application and its features. Based on this feedback the developers can take necessary actions that can help them to improve the efficiency and usability of the final application. It can also help the app developing companies to improve the reach of their audience.
- Machine learning is one of the most important parameters to initiate data-driven testing. In this process, the developers can analyze the data from previous test results and understand the most important failure points. Based on this data the developers can prioritize the test cases accordingly.
Integration of Platforms and Tools and Selenium Test Automation
The automation testers can use multiple tools and platforms to simplify the complexity of Selenium-based automation testing practices. Moreover, these platforms can help developers in improving the quality of their application testing practices. For instance, cloud-based platforms eliminate the need of having a physical device test bench as they can achieve similar test results while executing all the test cases over the web. These platforms also help developers to initiate and execute automation test cases from anywhere in the world. We can improve our knowledge about the influence of cloud platforms by considering the example of LambdaTest as a discussion point:
To complete the primary introduction, we can say that LambdaTest is a modern cloud testing platform that helps developers to initiate secure cross-browser test cases on web apps. With the current state of the app development market, cross-browser testing has become the industry standard for ensuring that you are providing the best performance to your customers. This is because cross-browser test cases verify the performance of the application irrespective of the user’s change in browser version, operating system, or device specifications. This platform also helps the developers to improve the efficiency of the test cases by almost fifteen times with the help of parallel test execution. Other than initiating the test cases from Selenium, it can also execute automation test scripts from other well-known frameworks like Playwright and Cypress.
LambdaTest is also beneficial for verifying the proper performance of internally hosted web apps for the company’s operations. Finally, the integration of hundreds of real devices with the existing thousands of simulation software helps the developers to massively improve the accuracy of the test results.
The Conclusion
The primary motive of this article was to spread adequate awareness about the improvement of Selenium-based automation testing practices. The inclusion of modern technologies like artificial intelligence and machine learning can massively improve not only the efficiency of the test cases but also the quality of the final application. Finally, app developers need to keep themselves updated with all the latest automation testing innovations. This will help them to maintain their company’s position in the competitive segment of app development companies. The app-developing companies can also hold frequent awareness campaigns and seminars to distribute the required knowledge about healthy automation testing practices. Lastly, app companies need to choose the correct tool or platform that can complement their development practices.
