The need for speed is nothing new in software testing. The application of agile and lean approaches to development have put testing teams under pressure to test functionality as quickly as it comes out, and with DevOps creating synergy between development and business operations, testing teams have had to meet the challenges of speed associated with continuous development.
Many testing teams have managed to meet the demand for accelerated testing, but how many can claim that they’d be able to keep up high-quality standards in testing if the rate of development accelerated even further?
Accelerate with AI
Given the veracity and the pace of change, new technologies, advanced capabilities and more innovative methods, many QA teams are looking to AI in order to capitalise on the efficiencies it offers by way of speed, innovation, and accuracy.
In testing, the ability to blend human expertise with AI-powered insights has never been more important: it’s about using the best person or bot for the job in order to keep pace with testing and ensure quality. For example, QA teams can indicate the prioritised user journeys to test first before switching to an AI-powered test mode in order to test other journeys that they might not have even considered. These could be focusing on modules that have changed or exploring areas that have not received adequate test coverage. Teams can now also automate the capture and analysis of runtime performance data and enable the testing process to “learn” where potential bottlenecks are in order to eradicate them.
AI is also enhancing application discovery capabilities. This means testing can now automatically build a GUI interaction model by routinely learning about the elements that compose an application screen. Companies can then use this information to automatically populate the automation logic in an application model.
Simplifying performance testing and device labs
In the past few years, we’ve seen subtle but critical improvements in load and performance testing products that teams should look to take on in order to keep pace as DevOps influences development, product and service delivery.
Performance testing is often a black art that requires specialist skills to set up. However, now techniques have evolved that allows test teams to scale up the load from the functional tests they have developed. Intelligent frameworks that can multiply the load from functional tests to create realistic load tests, put performance testing capabilities into the mainstream.
Similarly, testing the breadth and scale across a matrix of different devices, operating systems and browsers can now be simplified. Testers now have the ability to create a scalable ‘device lab’ that virtualises and manages thousands of devices, each with different OS and software combinations that can be run against different test cases and user types. The idea is that a test can be developed once and then run against a matrix of different real scenarios. And multiple tests can be cued up and multiplexed intelligently against this device lab.
Maximising the Team
The days of only looking at “does the code work” are in the rear-view mirror. Now more than ever it is vital businesses are measured by software delivering positive business outcomes and delighting customers. At the same time, the team are under cost pressure to deliver more with less in an ever-shortening time window. They must take advantage of automation while maintaining essential human influence where it makes sense in order to optimise testing.
By using AI, intelligent performance testing and device labs to test the digital experience, testing teams can keep the pace of DevOps, and improve time to market and customer satisfaction. This is without losing sight of the bigger picture, which is helping the business to understand and test the user journey and gain valuable insight into the entire user experience.
Written by John Bates, CEO at Eggplant
