It’s ok if you haven’t heard of AI and test automation in tandem. Many people haven’t. It’s a relatively new concept and while there isn’t much out there regarding this which you can read, in this article, I aim to highlight why AI testing is so important, how AI bots can be used in automated testing while also discussing some of the challenges we still need to solve in order to make the most of AI testing.
Role of AI testing
Hiring a good automation testing company is a must and thankfully, as the time is going by, organizations are starting to realize this. So, it’s an exciting time to be a tester. But what’s the role of AI testing? It will change how you approach testing and how it gets done. The first of many reasonable uses of AI focus on test management and automatic creation of test cases. It reduces the level of effort, with built-in standards, and keeps everyone consistent. Secondly, AI focuses on generating test code or pseudocode automatically by reading the user story acceptance criteria. The third option, codeless test automation, would create and run tests automatically on your web or mobile application without writing any code. Siri, Alexa, Google Assistant, these are all examples of AI, each with specific goals and roles. Similarly, for AI bots to work, you need to define a specific goal of AI – whether it’s creating generating test code, creating test cases automatically, performing codeless tests, or something else.
Training the AI bots
The idea of AI is to understand the environment, process the input to perform intelligent actions and improve itself over time. An AI-based keeps on learning with more and more data fed into it with the passage of time. With a smart algorithm, the system analyzes, correlates, and makes smart decisions that significantly minimizes human involvement. The same is the case with AI bots. To build them, we must train the bots to process input data by asking questions to perform an intelligent action, just like Android Auto Google Assistant.
Challenges with AI-powered applications
As discussed, this concept is still new and because it’s new, many unaddressed problems will only be resolved when more work will be done on it. When attempting to build AI-powered applications for testing, the following are the challenges and possible problems you may face:
- Identifying and perfecting all the algorithms needed
- Collecting tons of input data to train the bots
- The behavior of bots from input data
- Repetition of tasks even when the data inputs are new
- Training your bot is a never-ending process as we’re continuously improving algorithms
In many ways, AI testing is like teaching a child by example. It’s an arduous process, but one that pays off when done properly.
AI is no longer an idea. It’s a reality. Today, almost every automation testing company plans to integrate AI into their processes. If you take a moment to think about all the technologies we use daily, AI has already begun silently integrating into our lives. Get ready! Thanks to AI, the role of automation is on the edge of dramatic change. They may not quite be here yet, but AI test bots are well on their way.
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