You may have seen that we have raised $4m recently. What are we planning with this? We have a clear path ahead for product, with three different categories.
Today we support testing desktop browsers such as Chrome, Firefox and Internet Explorer, plus we recently announced our first Mobile Web platform supporting Android. Rainforest's platform support is designed to be expandable, over the coming year we're going add a ton more options:
OS X and iOS web will come first, in Q2 this year. You and our customers have asked for us to support these from the beginning, and supporting this will allow testing in Safari, OS X desktop apps, plus iOS mobile websites.
Native app is the next step - support will come later in the year, bringing the ability to do QA easily while using Continuous Integration. The whole experience will be close to what you see today, with a little more initial setup to get your builds to us. Post native app support lies testing on real devices.
Exploratory Testing is the biggest non-platform feature we'll be adding this year. This will allow you to direct testers to a certain part of your app, prompting them to think about how to test it. When they find anything wrong, they'll write a regression test to catch it and notify you. Simple. Customers will have less preparation, plus see important bugs faster than today.
Automation of tests is the biggest thing we never talk about; if you didn't know, today we're 100% manually powered - utilizing over 50,000 testers. We don't believe we'll ever remove the human aspect of Rainforest - it's got too many advantages - but we are going roll out totally seamless automation. This is the holy-grail of QA: No writing code, no maintenance of code, no flaky tests.
Last but not least, Stability and Quality includes all the work behind the scences to keep our systems up, running and stably producing you great results as fast as possible. Some of this includes improving our VM stack system - which runs all our virtual machines, optimizing our Heroku setup and improving our core algorithms for quality with the help of another Data Nerd.
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