Systems | Development | Analytics | API | Testing

While AI and machine learning have been industry buzzwords for a while, they are now becoming fundamental to software testing. Machine learning algorithms are making it easier to sift through logs, identify patterns, and even predict where bugs are most likely to occur. As these technologies mature, the role of AI in software testing will undoubtedly expand.

First published on the BSA site along with DXC Technology Data analytics and generative AI are revolutionizing the approach organizations take to software development, testing, and delivery, enabling standardization and scalability across the board, including test automation and management. Like other sectors, financial institutions struggle to match the rate of transformation necessary to maintain a competitive edge.

Many questions arise whenever adopting exploratory testing (ET), primarily due to misunderstandings about what it means. The fact that exploratory testing is mainly unstructured, especially compared to the traditional scripted testing approach that is highly detailed and restrictive, makes it harder to connect it to the word “plan.” Sometimes, users call it ad hoc. Does all this mean that we can’t prepare ourselves for exploratory testing and establish some level of plan for it?

Mobile test automation has become a crucial aspect of software testing due to the increasing popularity of mobile devices. However, the lack of simple and focused toolsets has resulted in a gap in the availability of effective mobile test automation skills in the industry.