Whether you are a software house, a bank, insurance or telco you have to be excellent at developing, testing, and delivering software. That is the reason why we keep talking about Agile, DevOps, Continuous Delivery, Continuous Integration. Nowadays it is vital to obtain Continuous Testing in order to achieve desired speed and quality.
Simple definition can be explained as the cadence at which you deliver production-ready software. How fast can you go from a high-level idea to something that gets deployed into production and starts making money? Or, how fast can you start delivering smaller features quickly?
To me, quality involves two things: am I delivering the right thing for the business and am I delivering it right (without bugs)?
So, how do you implement this concept of quality at speed? Formula One pit stops offer a good analogy for how testing needs to change. Testing is happening from the very first moment when they start thinking about the design of the car, during the week as they make adjustments to the car, as they’re driving the day before, and even during the race. It’s all about Continuous Testing. They’ve turned pit stops from what’s keeping the quality of the car really high to a differentiating element of the race, part of the strategy for winning the race. Even in many sports such as Formula one people are adapting the new technologies and its features. I believe people that’s how we need to think about testing.
What is the benefit of continuous testing for SDLC ?
The importance is beyond predictions because automated checks are faster and more reliable than people checking the software manually. Manual testing is often a boring job. We want to be able to deploy product changes on the same day that our Sales people ask for them. To make this possible we automated our deployment scripts, which also run our automated tests. A computer can carry out repeatable steps much faster than a human so automation helps us deliver valuable solutions more quickly.
How can we achieve it ?
The true answer relies on using DevOps implementations, such as Service Virtualization.