Automated test planning is a myth, and mobile AI is a reality.
Without human intervention, AI-related stuff is not going to make any sense in any way. AI can make test plans and even perform risk management. It’s actually happening with different capabilities. You can consider the newly released AI integration assistant (Chat GPT with Siri), where you can just ask, “Open the pictures where I wore the blue color dress and also I’m laughing.” Indeed, it can now. And one more example is Devin. I am not sure how it works, but based on the same demo video shared, it is more capable than any human. Automated test planning is indeed an AI capability.
When Gen AI (Chat GPT) was released, people started focusing more on AI, but in fact, before that, it's been traditional programming, ML, DL, then AI, and there were so many concepts involved. One of the best is neural networks (DL), and with this already, we can see AI can analyze mobile interfaces, like grasping all the screenshots while scanning or during execution, and based on this, it can improve the analytics part and even detect any errors. It’s happening right now. I guess I saw something similar to this on Applitools.
Happy learning!
A really nice idea so let me wrap it up in a very direct style
Myth: AI Can Completely Automate Mobile Testing
There is a common belief that AI can entirely take over the process of mobile testing, eliminating the need for human intervention. This myth suggests that AI can handle every aspect of testing, from performance evaluation to user interface consistency, with absolute precision and without any oversight.
Reality: AI Enhances but Does Not Replace Human Involvement
In reality, while AI significantly enhances mobile testing by automating repetitive and time-consuming tasks, it does not completely eliminate the need for human testers. AI tools can quickly identify performance issues, suggest improvements, and ensure user interface consistency through pattern recognition and data analysis. However, human testers are still essential for interpreting complex results, making judgment calls, and understanding the nuanced user experience.
Example:
- Myth: A company might believe that implementing an AI testing tool means they no longer need a team of testers, expecting the AI to catch all bugs and issues independently.
- Reality: In practice, the AI tool might effectively identify most common performance bottlenecks and UI inconsistencies, but human testers are still required to investigate edge cases, validate AI findings, and ensure the overall user experience meets the desired standards.
I hope you liked it as well.
