Front-end testing is a form of black box software testing in that it requires no behind-the-scenes understanding of how a software application works. It’s solely concerned with evaluating the user experience of an app. A front-end test is only effective if it tests both the functionality and visual appearance of an app, where “appearance” includes things like the layout of a page and the size, shape, color, and legibility of visual elements like buttons, form fields, and text.

I had the chance to finally sit down and find a way to inspect TLS traffic flowing out of an application running on my machine. Although I did not invent anything, I needed to put together a lot of different tricks in order to succeed, and the documentation I could find online regarding this process is scattered, at best. So, here we are with a guide on “how to inspect TLS encrypted traffic without going nuts”. Hope you enjoy!

Organizations today are looking to do more with less. The solution for many? Digital transformation. While digital transformation isn’t a new concept, the benefits of boosting efficiency, controlling costs, and delivering better customer experiences are obvious in today’s topsy-turvy economic conditions. Digital transformation often involves making the transition from legacy monoliths to modern cloud native microservices-based architectures.

What device should be used to run the tests? What operating system version, manufacturer and model to use? How to get these devices? These are some of the questions we ask ourselves when planning a mobile application testing strategy. If we consider automating these tests, other questions will surely come to mind.

Machine learning watching generative artificial intelligence (AI) take off feels a little bit like an American Girl doll envying the Barbie movie excitement from afar. What is she, chopped liver? But we can’t forget about machine learning, because it’s the giant that generative AI is standing on. How? Well, machine learning is how generative AI learns. Generative AI takes machine learning a step further by leveraging those learnings to produce something new.