Conducting software testing manually has a lot of drawbacks, this leads to the urgency of tech-solution that is secured, and able to work effectively and efficiently to automate the continuous process of software testing. RPA with AI/ML arises as a solution that solves the issue, offering valuable benefits to companies with how fast it works, the cost it reduces, and its interoperability. Examples of use where A.I. RPA can be applied beneficially in software testing would be in visual elements, prediction of software defects, prescriptive software testing, and many more. It is important to acknowledge real-life examples where automation successfully takes over the software testing process, to minimize the risks and challenges and optimize the result.
Takeaways from the topic:
- Identifying the Framework of A.I. Testing
- Acknowledging the real-life examples/case studies where the software testing process is automated through A.I. RPA
- Identifying the key risks and challenges in automating software testing if done with AI-powered automation
- Garner successful tips for adopting AI-powered automation in software testing