Blog & articles

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.

In this article, we’ll be discussing what regression testing is, why it’s helpful, and how to do it. Regression testing tells us how unchanged or old features will be affected when adding a feature, deleting a feature, modifying a feature, and fixing defects. In regression testing, we re-execute the same test cases in different releases or builds or test cycles to make sure that changes like adding, deleting, modifying feature, and fixing defects is not affecting the other features.

What makes test automation key in bringing products and apps up to speed is that it's fast, efficient, and can cover a ton of ground. However, the main challenge of test automation is articulating a test script (the output of the test analysis process to exercise test cases) in a way that a machine can interpret for execution purposes, essentially creating machine-readable test scripts. Usually, companies hire skilled automation engineers or SDETs to rearticulate test scripts in coded form. Automation for most organizations is a translation exercise from human-readable form to machine-readable form, essentially re-articulating the output from the test analysis process in another form. There are apparent efficiencies to implement in this process.

Understand the 5 key characteristics of testability in software engineering to enhance your testing process and improve software quality.