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I was a QE on the Rackspace Auto Scale team. I would like to take you through the experiences and learnings I have had with testing Auto Scale, and how the ability to test it improved drastically using Mimic, an API-compatible mock service for Identity, OpenStack Compute and Load Balancers.
Many companies – large, small, public, private, start-ups – are looking into test automation, continuous integration (CI) and continuous deployment (CD) to get their code to customers as efficiently as possible, while not sacrificing quality or reliability. They realize that automation is essential for them to scale up and be competitive.
First, try to say the title of this post five times fast. If nothing else, it makes for a new tongue twister. It may sound silly, but that little exercise draws attention to the notion that for many companies, testing can be challenging and time consuming – along with being difficult to say quickly.
Testing is a good thing. It has to be – the world is telling us it is. There are conferences and hundreds of meetups around the world just to discuss testing. There are workshops and countless testing tools. There are tons of books explaining testing down to the very last detail. And, most of all, there are QA jobs. Testing is so vital that an entire discipline of software development is dedicated to it. Clearly, testing is really important.
I go to the gym and lift weights because I like the feeling of getting stronger and better. Two months ago, I started to feel a throbbing pain in my knees, yet I continued to go to the gym.
This is a guest post written and contributed by Daniela Sztulwark (@danielaszt), marketing manager at BlazeMeter, a Rackspace Cloud Tools partner. BlazeMeter ( is a self-service, web and mobile load testing platform (PaaS) providing developers an enterprise grade, out-of-the-box load testing solution and is 100 percent compatible with the popular open source softwares Apache JMeter™ and Selenium.
Everyone wants to release the next hot app or bulletproof their website. But until now, it took a unique breed to thoroughly test them and to ensure they’ll perform at scale. That’s because functional and performance testing have traditionally been challenging, complex, costly and time-consuming.
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