Intelligent Test Automation (ITA) is a software testing method that uses AI algorithms to believe the scripts and make the automation more robust and robust in analysis to enable better deboggage and more effective decision-making, among other things.
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Why Teams Adopt Intelligent Test Automation
Software testing keeps teams on their toes, from writing scripts to maintaining and finding the root causes of test failures. Unfortunately, manual testing on multiple devices is the enemy of fast and agile development.
With intelligent test automation, teams can alleviate much of the tedious work because AI and ML are behind them and working on their behalf. Testing teams perform many functional tests on multiple devices in hours, not days. It means they are more focused on the job they love (which happens to be the job that matters most). With faster feedback, developers can quickly fix bugs and get code back to the testing team.
The data is only as good as the subsequent analysis. Test teams create incredible amounts of test results. Giving meaning is a task in itself. Intelligent test automation and analytics find ways to reduce excess data into actionable insights.
Today’s software and applications must be free quickly and continuously as processes and devices become more complex and fragmented. When your test feedback speeds up, so do your results. Intelligent test automation and analysis replace manual processes with autonomous ones. As a result, teams can move faster, decide faster, and shoot more quickly.
The Most Common Challenges of Test Automation
However, test automation presents some challenges, especially when first introduced.
- Creating complex tests results in a high proportion of manual tests.
- Reporting noise as false negative results in irrelevant test returns.
- Automation of unreliable tests makes a high percentage of manual tests.
- Running parallel tests while scaling requires significant IT investments.
- Operating an in-house laboratory is expensive and difficult to maintain.
These common challenges are easily overcome with the right strategy and automation tools.
Real-World Examples of Intelligent Test Automation
Now that you understand what Intelligent Test Automation is and what it can do let’s look at some examples.
Intelligent test creation
What if AI could help build test automation?
In a product like 21 Labs, the AI can learn an app. Specifically, the functionality and navigation of each screen based on the tests created. It allows teams to add assertions, change test data, reorganize flow, and be up and running in minutes. Of course, you can adjust if necessary.
These tools are based on standard testing frameworks such as Appium and Selenium, making them very generic for authoring across platforms and development frameworks.
Intelligent Test Maintenance
Simple changes like moving a button to another side of the screen can be significant for the user experience. But such changes can be an essential productivity drain for anyone on the quality control side of the house.
One of the big game changers for teams was the self-healing abilities introduced by the AI. In technologies like Perfecto Scriptless, an algorithm automatically extracts over 90% of the changes (like the ones I mentioned above) in an application. Moreover, the test does not stop at a single shift. As a result, testers can work on new tests instead of correcting old ones.
Intelligent Test Automation Analytics
As I mentioned, teams, especially those that run continuous or large-scale testing, generate mountains of test results. Moreover, it burdens everyone responsible for analysis, who has to assign and prioritize what should stay in Jira, for example.
In a product like Perfecto, ML makes the analytics aspect much brighter. The reporting engine can detect and categorize errors and then separate false negatives (caused by pop-ups, items not found, server failure, etc.) from absolute mistakes that must be secure.
You can literally and figuratively reduce data noise and make analysis much more productive.
Intelligent test automation drives testing. That makes scaling and quality easier, but, at the same time, it has limits.
Although AI and ML have vastly improved testing, many quality activities must be approached and solved with nothing less than the human mind. Autonomous processes, for example, always require human control.
But there’s one key point I want to make with this blog: intelligent test automation empowers testers to be more efficient and strategic about the things that matter.
Also read: We Are Using AI In Test Automation
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