Blog & articles

Learn about three essential test automation design patterns to enhance your testing strategy and improve software quality.

When starting the journey of testing out your software to improve quality and customer satisfaction, companies and stakeholders might have a lot of questions and require help answering them. Perhaps the first thing that comes to mind is not necessarily how to write test cases, but rather “What are they?”, “Why are they important?”, and “When should we do this?”.

In an ideal world, every code deployed into production would be without defects, but we don’t live in a perfect world; humans still build software for other humans. In our practical world, testing catches those defects in a last line of defense before the software reaches the end-user. It’s a big responsibility.

Two of the most recent buzzwords in the software development industry are Artificial Intelligence and Machine Learning. While these terms are often used interchangeably, there is a distinction. Artificial Intelligence (AI) is the broader concept of machines being able to carry out tasks in a way we would consider “intelligent.” Machine Learning is a current application of AI-based around providing machines access to data to let them learn-modify it in a way that benefits them. But what’s the role of AI in software testing?

New API testers may find themselves feeling lost when they first start API testing. While the process might seem daunting, there’s no need to worry. The chances are that new API tests have all the information they need already!

Understand how agile technical debt affects test automation and discover strategies to manage it effectively for better software quality and team productivity.