Machine learning has truly changed our world in many ways. Not only has it penetrated every sector of the society but also impacted in ways that were hard to imagine at one point in time. With deep learning in the picture, things have further resolved and many questions of the world seem answered like never before. Moreover, Machine learning models are bringing the kind of insights on the table that has the potential to change the way organizations do business or understand their customers.
Take for example the problem of understanding customer behavior. We all know how crucial it is for a business to know about their customers and form strategies around their behavior. This not only helps in keeping them satisfied but also preps the business for what may follow in the market. Especially in a time when customer demands change rapidly in the market and are disrupted by tech titans as well as small entrants, ML can be the difference between the success and failure of a business.
They can shed light on customer behaviors in the future through predictions that can help organizations stay abreast of them. They can tell you about the inventory that might be used up in the future or the number of sales that you should expect during a particular period. And this is just one use case. ML is not only a brilliant tool for peeking into the future but also automating processes in an organization itself.
Why ML Algorithms for Automation Testing?
When it comes to software development and testing, the transformation with cutting edge technologies like artificial intelligence and machine learning has just begun. As time flies by, it is imperative to know that traditional testing methods will remain intact, but new use cases of machine learning and artificial intelligence will just crop up.
The point is that in the past years the entire scenario of the market has changed. Businesses want to deliver their products rapidly to the customer. They don’t want to take years to develop one single product and watch customers happily embrace it. With the level of competition out there, businesses had to shift from a waterfall approach to agile methodologies. This led to the DevOps culture, where things were much faster than their traditional methodologies and both the companies and customers with satisfied. But, a successful DevOps culture required a much greater degree of automation in the business that began right from development to testing in the pipeline.
While organizations came up with new ways to build their software applications by breaking them down into smaller chunks, it is the quality analysis that has suffered. After all, with new technologies like the Internet of Things in the picture, testing applications have become one of the most troublesome tasks. There is a whole new generation of devices out there that are interconnected with others. Thus, keeping up the pace of testing for such devices is a tough nut for the businesses to crack. That’s where artificial intelligence and machine learning algorithms come into play.
Limits of Traditional Test Automation
To handle the testing challenges we mentioned above, organizations and quality analysts have come up with code-based test automation cases. Some of these are excessively popular and used by organizations for automating their testing daily. For example, some of the leading open-source test automation frameworks are Selenium, Appium and other code-based scripting solutions. But, there’s more to what meets the eye. These solutions have no doubt a great set of practices, knowledge bases and documentation but there are a handful of issues that one can’t just simply overlook.
- As the app changes, they tend to be flaky or unstable at times.
- They utilize frameworks that are well integrated into the development environments of test engineers and developers.
As a result, the QA teams turn to machine learning algorithms to resolve these issues and find more stable, responsive and fast test automation solutions.
Where Is ML Used In Test Automation?
The point to note with code-based test scripts is that they often kill the digital quality of your application. Most of the time they are a result of poor coding skills and reduce the confidence in test automation scripts. To start with, you must know whether you have flaky and unstable test scripts running in your testing pipeline. You’ll know when your test cases are inconsistent in each run or platform. Similarly, your tests might not use stable object locators and might not be able to handle environment-related implications. Moreover, developers are pressed with delivering a product at a much faster rate and the agile feature teams lack the skills to create automation scripts within the sprints.
Increasing Test Automation Coverage and Accelerate Time
As one replaces the manual QA testing methods with ML use cases, it increases the overall test automation coverage of the application. This also reduces the risk of defects crawling into production. But for this to work efficiently, you must properly scope the ML test automation suite with team members to focus on the right problems and avoid any duplicates. Statistics suggest that ML-based test automation solutions are at least 6 times faster than code-based testing methods. This directly means a faster time to value. Moreover, ML-based test automation is just a record and playback process that possesses built-in self-healing algorithms. They do not require heavy maintenance as compared to code-based testing methods.
Even though ML-based test automation solutions are a little less flexible and possess certain integration issues, they are great to help you enter a truly agile environment. And this is just the beginning. With time a lot of new test automation cases will come up, easing the processes for enterprises and quality analysts.