Is Machine Learning All Set to Replace Selenium Automation Testing
Artificial Intelligence (AI) as well as Machine Learning (ML) are the latest buzz in the field of digital transformation. But are they ready to replace and take over Test Automation done with Selenium? Let’s find out!
Selenium is known to be a web-based testing tool. It is an open-sourced tool widely used in comparison to the other licensed ones. It, being an open-sourced, portable framework; could be easily used for automating applications.
Some of the features of Selenium are as follows:
- Highly flexible in regression and functional testing. Regression testing mainly helps in executing tests against altered applications. These can check the running of these altered codes accordingly. Whereas, the functional testing helps to test the business requirements against the software and ensures that there is no defect with the software.
- These tests could be written in various programming languages, for instance, C++, Python, Ruby, and Java.
- Selenium easily supports cross-browser testing. This enables test cases implementation across different browsers a number of times.
Both AI and Machine Learning are not only simplifying software automation but also contributing to the complete makeover of the tech industry. Since the conception of AI/ML, the future of the Selenium Test Automation seems like the setting sun. Even though both have unique features to support their existence, it is hard not to look towards Machine Learning for some phenomenal leaps in technological advancement.
Are you wondering how? Here, are some examples of how Machine learning can change the face of test automation by preventing some of the cases:
- Manual work for writing test cases can be reduced
- In case, the test gets brittle, it may lead to false failures because of the framework skipping steps or dropping some of the tests.
After going through Selenium test automation, one would find Artificial Intelligence a completely contrasting concept. With respect to AI, human intelligence used by machines that mainly deals in simulation is a fresh concept for many of us. This technology allows the system to work on the assigned tasks that must be performed by the user. AI technology can easily transform and develop businesses.
Some of the features that help in better understanding of Machine Learning are as follows:
- Bots are being used by various organizations as an alternative option for financial prudence.
- Machine Learning is the best option for tasks committed by programs that require repetitive execution.
- For operational activities, data extraction and calculations, Machine Learning is being considered for helping with business processes for different organizations.
- From the security perspective, test automation using Machine Learning can protect the user data more effectively and reduce the risk factor to a great extent.
It was long back when vendors like the Silk test and WinRunner dominated the market. It was after that the Selenium came in and boasted about focusing more on clearing the programming issues and plight of the developers while they created automated tests. Even then, the testers were not satisfied because of Selenium’s high maintenance and flaky tests that drove them crazy.
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Also Read: Best Practices For Selenium Automation Testing
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As Google CEO, Sundar Pichai said in the Google conference, “We are moving from a mobile-first to an AI-first world”. He was very fore-sighted in saying so. The world is changing at a great pace and our future is getting largely dominated by human intelligence stimulated machine learning. AI automation being leveraged by Machine Learning is all about using spidering to write the tests automatically for the better application.
AI-assisted automation tools
Some of the automation tools that use AI-assisted technology and are leveraging machine learning are as follows:
SauceLabs
SauceLabs is one of the world’s biggest continuous testing cloud for mobile and web applications. It works towards providing a flawless digital experience by furnishing scalability, coverage, and analytics quickly. It has access to a large reserve of data that can come up with insights and leverage Machine Learning.
SauceLabs allows the testers to run their tests on more than 700 different browser platforms, in the cloud. It is one of the pioneers in using Machine Learning in automation testing. Sauce Performance, an addition to the original platform, helps the developers to measure the performance of the application metric in the early developmental phase of the software.
Applitools
This is another tool making a big difference in the field of Machine Learning. It has opened new innovative ways of testing. It works on the idea of visual validation testing. It brings to use a sophisticated and intricate algorithm to bring out potentially active bugs on the applications without the tester specifically bringing out the elements.
With Applitools, it is easier to create visual testing as it can be done without requiring percentages, configurations or visual processing settings. The adaptive algorithm of this tool makes it stand far above the reach of Selenium automation.
Testim
Another tool to leverage Machine Learning is Testim. It is known to speed up the process of execution, authoring and maintenance of automated tests. It reduces flaky tests and goes through intensive maintenance themselves that was the main challenge faced with Selenium Automation testing. So, most of the organizations are becoming inclined towards bringing machine learning to the picture and leaving Selenium testing behind.
Test.AI
This tool is way different from the rest as it has been designed to combine the AI brain with Appium and Selenium to produce the best of both worlds. Here, there is no need for messing with the element identifiers and no requirement for codes. This AI automatically helps the applications to execute test cases by just identifying the elements and screen dynamically in the application.
It is a smart tool that helps to identify the changes in the elements itself and adjust it by making manual changes with the tester. Even if this tool is still in the Beta stage, it has a lot of potential to change Machine Learning as we see it.
Other tools that make it to the list are:
- Mabl
- Sealights
- ReTest
- ReportPortal
There are many organizations that are making a shift from Selenium to Machine Learning for automating web applications. One can shift to ML and AI technology completely or they can try and create custom solutions that can adjust with existing Selenium automation. This could be a costlier process that would need hiring good engineers specializing in ML, writing codes and performing intricate procedures.
Why is Machine Learning gaining importance?
With Machine Learning, a lot of positive changes are taking place that is making it acceptable throughout the software testing world. It is necessary to understand the way ML works in the day-to-day scenario to make automation safer and more efficient.
Domain Model Creator
Having to train a Machine Learning-based algorithm, one must come up with a testing model. This may need someone who has domain knowledge and efficiently indulges in creating models to help in development endeavors. This change has made it easy for those who know how to automate by analyzing and gaining an insight into the intricate data structures, algorithms, and statistics.
Testing of UI Interfaces not necessary
There is another change brought about by Machine Learning that has made the difference in the world of automation in the absence of user interface. Being backed by Machine Learning, automation has been made easier for many engineers who were initially dependent on testing UI interfaces.
Automated testing by Visual Validation
Machine Learning works on pattern recognizing techniques. The most popular pattern is the image that is used by automated tools for visual validation. Its tools like Applitools use Machine Learning to detect differences that may be missed out by the testers. This tool makes sure that the user interface directly faces the user and recognizes the features such as color, eyes, size, and shapes without overlapping or hiding other user interface elements.
Running only the important tests
Many times, the entire test is run, but the minor fault remains hidden. This is not the case with ML. Many big, global companies are taking the help of AI tools through which ML is used to pinpoint the exact error that needs to be rectified. This saves a lot of time and helps analyze flag areas and current test coverage.
Spidering AI
Another popular practical application of AI is the use of ML to automatically write software tests by spidering. For instance, the latest AI/ML tools like Mabel helps to point to your app and automatically begin crawling.
While the tool is crawling, it also collects data relevant to features such as capturing screenshots, downloading the HTML code for every page, calculating the page loading time and so on. It runs the same steps again and again. Over a period of time, it builds up a comprehensive data and trains your ML models for the expected patterns of your app. On running it, it compares the current state with all the known patterns that have been learned before. In case it notices deviation, such as JavaScript errors, a visual anomaly or slower page loading speed, it will immediately flag it as an insight or potential issue.
Even though this approach is still in its infancy, according to Oren Rubin, the CEO, and founder of ML tool Testim, the future is bright for this method. Thus, it can help us in understanding which parts of the application need testing. ML tools can do extensive and tedious work while the human tester can simply verify it.
More Reliable Automated Tests
Selenium or UFT tests often fail due to changes made by the developers in the application. It can be something as simple as renaming a field ID that ends up failing your test. The current solutions are chiefly based on one selector or one path that basically uses only one way to find fields in the app. However, AI tools can employ advanced ML in order to adjust to these changes automatically. This, in turn, makes our tests more reliable and easy to maintain.
Current AI/ML testing tools can learn about your application, understand relationships between parts of the DOM and learn about changes in the framework with time. Once it begins to observe how the application changes, it can automatically decide which locators should be used to identify an element during runtime, without you having to lift a finger. Furthermore, if your app modifies, the ML tool can learn the script and automatically adjust itself accordingly.
In summary, Machine Learning has already brought about some dramatic improvements in the field of technology, but it still has a long way to go. To deliver the highest level of understanding and accuracy, it still needs the assistance of human intelligence. It has its limitations, but the future looks bright for the technology.
