Today, almost everything seems to be AI-driven or influenced by Artificial Intelligence. From smart assistants to self-driving cars, the involvement of AI technology has transformed lives and proved to be a game-changer across industries. It has helped businesses automate and simplify their operations, and boost efficiency. One of the areas where AI has made a remarkable impact is in software testing where its implementation has made the testing process faster, easier, and explicit.
Up until now, QA engineers were mainly dependent on manual testing which was a time-consuming process, and yet the software could not be tested thoroughly. They were required to perform fresh tests every time the software demanded new additions or updates in the test code. Thanks to automated testing that now allows QA engineers to test a product with minimal manual efforts; ensuring maximum efficiency, and yielding top-notch results.
However, test automation isn’t just enough when it comes to high-quality software testing as you need to invest time and effort to write test scripts. The blend of AI and test automation tends to provide a relatively better outcome. It can help identify changed controls and even the slightest alterations with the algorithms. Moreover, with agile software development in place, which is said to have coincided with the rise of automation, QA teams can deliver robust and bug-free software in small batches; paving the way for a fail-safe product.
AI testing is concerned with cognitive automation; a subset of AI that leverages advanced technologies like natural language processing, data mining, text analytics, etc. to address the associated challenges with human intelligence. In this article, we will walk you through ai testing and learn how AI technology has revamped the testing process.
A great application of AI in software testing can be witnessed while writing test cases which has now taken an automated approach. It involves machine learning to automatically write test cases for the software by spidering. All it requires is to point the AI/ML tools to the application and it will automatically begin the crawling process. As the tool crawls, it obtains some useful data such as screenshots, the HTML of each page, and the page load time. The cycle is continuously repeated until enough data is captured to train the ML models to comprehend the patterns in the application.
Later, when the tools are executed, they compare the current state of the application with the patterns they have learned. If any sort of deviation is encountered such as visual difference, slower page load time, or some other page errors, the tool marks it as a potential issue. However, in case of valid differences, for instance, if there is a new UI design change, the tester will have to intervene and validate the issue flagged by the tools as bugs.
Among the testing methods that use AI and ML, one which is getting highly popular is image-based testing using automated visual validation tools. This type of testing is performed to confirm that the UI appears correctly to the users. This shouldn’t be confused with functional testing tools which were designed to test the functionality of the product through the UI. Visual testing would ensure that the UI not only looks appropriate to the users but is in the right shape, size, position, color, and does not overlap with other UI elements.
Many of such types of tests are highly challenging to automate; sometimes ending up in manual tests but that’s what makes them a perfect fit for AI testing. Leveraging AI/ML-based visual validation tools would allow you to observe the differences that a QA tester would most likely overlook. A simple ML test can help you automatically detect any visual bug in the application and validate the visual correctness.
Conventional testing still demands human resources for data and source analysis. Needless to say, even expert QA engineers can make mistakes or miss out on the bugs. But that’s alright! Because of substantial data, testers can sometimes lose their focus and overlook certain significant defects. Consumers notice these bugs even before the project stakeholders and this can eventually harm the brand reputation and product positioning in the market. That’s when machine learning in software testing comes into play. They train systems to grasp source analysis and apply the acquired knowledge in the future. This AI testing technology helps testers yield more accurate outcomes besides eradicating human error probability, reducing the test run time, and finding every possible defect. Ultimately, the QA team is not burdened with large volumes of data to handle.
AI and ML in software testing tend to produce more reliable tests. Many times tests fail due to some changes made by the app developers. Now, ML-based tools can automatically adjust to these changes and make tests more reliable and easy to maintain. They can learn about your application, identify changes with time, and understand the relationship between the elements. This helps them make automatic decisions at run time as to what locators need to be used to recognize an element. And even if the changes are frequent, the ML script can adjust itself automatically; reducing the script’s flakiness.
The amalgamation of AI and test automation provides a remarkable enhancement in the overall scope and depth of quality assurance tests, leading to significant improvement in software quality. Test automation can effortlessly gain access to memory, file content, data sets, etc. to determine the software’s working status; whether it is working as intended or not. AI-based automated testing has the potential to execute over a thousand test cases related to each test run offering coverage which is likely impossible with manual testing.
If you have made a change in the piece of code, then using AI/ML tools, you can precisely identify the minimum number of tests required to test those changes. The tools can inspect the current test coverage and point out areas that have less coverage or are at risk. In a nutshell, it can help you pick the right set of tests to be executed for code changes to offer quality test coverage.
It’s a known fact that manual testing is time-consuming as well as expensive, and repeating tests is a daunting task for QA engineers. It is also not that efficient in providing or maintaining a speedy test process. But integrating AI or ML in software testing can facilitate time-consuming and repetitive tasks by automating them. This provides a sigh of relief to developers as the workload of writing test scripts or investigating large data sets is now managed more quickly. Every time the test is automatically repeated, the source code adjusts itself to fix any bugs or defects. This helps get rid of the additional costs involved in repeating tests and accelerates the testing process, eventually saving you on time, money, and effort, and helping you launch the software in the market in a stipulated time.
With its ability to automate even complex tasks, provide more accuracy, and bring some massive advancements, AI has made a significant impact in software testing and proved an invaluable asset for businesses by making the testing process simpler, quicker, and better. It allows testers to leave behind the traditional approach and adapt the AI-powered testing process. With AI technology reaching new heights every day, we can surely expect some crucial enhancements in software testing in the future. It’s high time to switch to AI testing.
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