Testing plays one of the most crucial roles in developing a successful IT application. But to ensure error-free development of software, QA teams are required to station robust testing strategies and one of them is deploying AI and ML tools in the testing process. The recent rise in AI-based testing methodologies has helped QA teams to efficiently manage large and intricate IT systems with much ease. The fact is that AI and ML algorithms provide improved and more effective test automation results, taking off the burden on test teams of refining and performing the test cases multiple times. Want to know more about how AI and ML help in achieving test automation? Do read this post till the end.
Also Read: 4 Best Ways to Automate Microservices Testing
Source – Medium
Writing test scripts is one of the most exhausting and grinding tasks involving skillful hands with prior experience in coding test cases on programming languages like Python, Java, and Ruby, among others. This entire process does not just need a lot of effort but is also time-consuming.
Quarks has its own in-house developed tools which use manual test cases to provide selenium automation testing scripts. Our AI-enabled testing platforms are capable of reading manual test scripts and developing automation scripts on their own. The AI algorithms utilize the Natural Language Processing (NLP) technique which fully understands the intent of the testers and imitates their actions on the application. But do you know what is the best part here? Well, this entire process is executed with the tester not having to write even a single line of code. This lessens the time and effort taken to write the test scripts by up to 80%. So, this is how AI and ML help in achieving test automation.
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Source – Stickeyminds
AI and ML help in achieving test automation by using self-healing techniques. This approach of automation testing resolves significant problems involving test script maintenance in which testing scripts are split at all stages where there are changes in the object property, such as name, CSS, ID, etc. This is exactly the place where dynamic location strategy plays a crucial role. Here, these changes are automatically detected by the programs and are fixed without human interference. This transforms the entire process of test automation by allowing teams to employ the shift-left technique in testing methodology making the process proficient along with providing faster delivery and improved productivity. A small instance explaining this includes the way the UI identifier gets automatically rectified in the test case whenever there is any modification in the object identifiers by the developer on the HTML page.
The AI engine traces these elements even when there are modifications in the attribute. After this, it changes them accordingly as per the modifications done in the source code. Using this self-healing technique, developers save a lot of time that they have to put in firstly to identify the changes and then make the changes in the UI at the same time.
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Source – Graphics Viewer
There has been a drastic rise in demand among users for speed, making organizations to look at agile to expedite their efforts for application development and delivery process. However, sometimes it gets struggling for many testing teams to match up their pace in line with these efforts. Most testers exhaust a lot of their time in mending malfunctioning automated tests or performing manual UI tests. This results in delayed release cycles as the codes are not fully tested by the testers or they are launched as it is with defects. So, in a place where a company opted for an agile process with the intention to improve its quality turns out to be worthless and ineffectual.
But here, AI and ML help in achieving test automation by changing manually running UI tests into scriptless and automated API test techniques. The process takes off a lot of burden from testers’ shoulders by minimizing the manual steps and improving the turnaround time. AI and ML-enabled API testing prove to be a bane to uncover pesky bugs and offering an in-depth understanding of the application.
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Being in the QA industry, can we ask you, how many times you perform the same task just to avoid the chances of any false positives or to attain consistency? Well, the fact is that there are many tasks in the testing process that are inescapable even when you are using the most effective test framework. This is where AI and ML help in achieving test automation by providing some relief to the test teams by automating many of their monotonous jobs. Daily tasks, such as developing data models, mockups, noting page classes, and extracting test scripts from architecture documents are to name a few tasks that can be automated using AI in test automation.
AI and ML help in achieving test automation to a great extent as there are a plethora of use cases of using AI in testing which can help QA professionals to understand complex applications with multiple layers, develop intricate simulations to predict the results and handle big data. There is enormous potential in AI and ML which can be harnessed by the tester to learn, experiment and achieve test automation.
Quarks is one of the organizations in the country offering QA as a service and takes immense pride that it has been applauded by its clients for its digital transformation capabilities and quality engineering services. Have a question? Send an email to firstname.lastname@example.org.
Also Read: 4 Best Ways to Automate Microservices Testing
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