The weather, the tennis, the football — with all the distractions, you’d think those of us on the Real User Monitoring team would be kicking our feet up, right? Not a chance! I'm super excited to tell you about our latest release: a brand-new version of our Performance Trends Report.
While it may seem like a distant prospect, Black Friday is coming and retailers are busy preparing for the busiest shopping period of the year. The holiday season is normally a busy time for us too, as we start carrying out performance tests on retail sites to get an idea of how they’ll behave when unprecedented visitor numbers put systems under equally unprecedented strain.
Some of my customers are trying to design an automated script to perform specific workflows with a predicted outcome. Unfortunately, the automated workflow they want to execute has many variations in their environment, and they’re having trouble creating a dynamic, automated script that handles environment deviation.
On May 21, 2018, Bank of America announced that it was rolling out its chatbot, Erica, to all its mobile customers. On the surface, the premise makes sense. It’s making the bank more relatable. It’s providing real-time customer support to people where artificial intelligence (AI) assistants like Siri and Alexa are becoming the norm. It doesn’t have the limitations that some phone-based IVRs have, and it aims to provide immediate assistance instead of making us wait for a human (we’ve all shouted “representative” or pressed zero dozens of times to get a real person). Erica is a great way for Bank of America to optimize the customer experience.
But let’s pull back the covers and ask some basic questions. How does Erica know the customer so well? How does Erica pull from different sources of information? How does Erica know what products and services to offer? What systems, both homegrown and third party, does Erica need to be effective?
Quality assurance (QA) used to be a compliance activity. You were releasing a product and needed to test it and stamp it “approved.” QA was about testing that the code worked. You might manually test the code. You might have even tried some automation — coding a set of test scripts that would try to capture regressions or errors that you had eradicated in the past, but which somehow crept back in. All in all, you were reasonably satisfied that you achieved a level of test coverage that met your goals. Then, you put your code into production and crossed your fingers that nothing went wrong. And if it did, you tried to fix it as quickly as humanly possible.
Note: Test engineer Reena Kuni and software engineer Bekki Freeman also contributed to this blog.
On the Eggplant Functional team, the relationship between Dev and QA is very collaborative. We work closely together, use our Slack channel to organize regular walk breaks together, and frequently talk about ways to increase product quality.
It used to be that software testers could test their applications on just one platform, and only have to worry about testing that the code worked.
It’s no secret that the digital revolution is quickly changing the way businesses and customers interact with each other. Like Blockbuster, companies that don’t understand the evolving needs and tastes of their customers will die, while companies like Netflix that fail fast, quickly adopt technology, and evolve, will thrive.