Hello to the Testhouse family! This is what I hope will be the first in a series of blog posts aimed at sharing insights into things I have learned or experienced in my career in testing to date. If you find anything that sparks your interest, or have follow up questions, then please do let me know. This first blog is about mentorship, conferences, and the value of networking; I hope you find taking a few minutes to read this will be time well spent!

QA teams and developers have started shifting focus to an open-source test automation tool and framework. Open-source test automation frameworks incorporate the most effective software testing techniques, tools, assumptions, and libraries that may be utilized by many teams. Aside from Selenium test automation framework, another prominent automation testing tool is Cypress.io.

In the past two weeks, this series has taken a look at how often consumers encounter errors while browsing online and some of the behaviors a user will display when discovering a bug. If you’re new to the party, I’ll encapsulate it here by saying mobile apps are rife with coding issues and the vast majority of consumers have little to no tolerance for low-performing software. What we haven’t discussed yet is the ripple effect of one user encountering a problem.

I am back with blog 3 in this series! In blog 1 I introduced the concept of the Digital Complexity Paradigm (DCP), and in blog 2, I broke down the DCP by focusing on the Technical Complexity factor of that formula. As covered in blog 2, using my favorite “digital transformation of the broom” example, the Technical Complexity enabling today’s digital is getting more and more challenging, and there is no end in sight. That in itself can be a mountain to climb.

Ever wondered, how do you measure testing productivity or testing effectiveness? When to say testing is complete? In all such questions, QA metrics come to the rescue. QA metrics allow test leads or managers to take decisions on various situations encountered in the project life cycle. Tricky part is that QA metrics make sense only in the given context. For example, for one project 95% pass percentage is good to go live but for a safety-critical application anything less than 100% is life-threatening.

Have you ever wanted to load test an application behind a corporate firewall? If so, you probably ran into a few issues. Load testing on the cloud can be the most cost-effective way to run a load test, but not if the application isn't publicly accessible. Recently, we released a new feature for k6 Cloud that allows you to run load tests on your own private load zones. But first, does it really matter where load is generated from?