A few weeks ago we brought you a rundown of our five favourite automation testing tools for Android, and the response was overwhelming. So we’ve written a follow-up article, this time for iOS developers. The testing challenges vary slightly for Android and iOS testing. For one thing there are far more Android models out there than iOS, which means lots more potential bugs. Also, the two variants have their own default languages, which further complicates things.

The next generation of BI will heavily integrate machine intelligence and AI – and how these technologies are embedded and used will matter. The best solutions will support human-centered analysis, which takes advantage of new technology while not losing sight of value of the human perspective in decision making and analysis.

We hope you’ve had a fantastic summer and are now enjoying the fall. After our release of Bugfender 2 earlier in the year, we’ve been fine tuning and adding many UI improvements. We’ve also been working on an improved filter view, enhancing issues and adding React Native support – all coming very soon, so watch this space.

When we think of other products in Bugfender’s space, we don’t really think of competitors; we think of alternatives. Because, being honest, Bugfender doesn’t really have any direct competitors. That sounds like cheesy marketing, right? Yep, we get that. And we don’t say it to be arrogant. We say it because Bugfender was built to provide a unique service in our space. In fact it’s designed to serve the specific needs of our parent company, Mobile Jazz.

Developers are a fussy bunch. We sweat on the tiniest details and every aspect of our product has to be just right. We don’t like entrusting our precious designs to anyone outside our inner circle. Yet we’re increasing delegating key quality assurance (QA) tasks to robots. The market for automated testing products is expected to be worth $20 billion by 2023 – three times as much as now.

Today, Docker or Kubernetes are obvious choices. But, back in 2015, these technologies were just emerging and hoping for massive adoption. How do tech companies make the right open source technology choices early?