Intelligent testing: The radical new business disruptor

Forget coding, programming or even security. How businesses use the intelligence they get from testing their core products will be fundamental to their success in 2018. For successful online e-commerce businesses, this isn’t about testing code, but about using intelligent testing to determine whether the business is actually achieving its objectives.

Intelligent testing is the combination of AI, machine learning, and analytics coming together to help businesses address the unexpected and to mitigate against traffic and cybersecurity issues that can ground even the most sophisticated website. In simple terms, intelligent testing tests, monitors, and fixes business problems and ensures that business applications are achieving their defined business goals.

Intelligent testing is also about demanding more actionable intelligence from testing: when automation, cognitive systems, and advanced analytics become integral parts of a testing ecosystem, we should have great expectations, especially when proactively dealing with issues surrounding user interfaces and user experience. Businesses who stop at ‘this software works and meets user expectations’ are really selling themselves short.

When it comes to intelligent testing, it’s not about testing whether the code or application works, but whether the business works. Intelligent testing can inform every area of your business development, including exceeding your sales targets and beating your competitors, to name just a few critical drivers. When you’ve got advanced analytics in place, there’s no excuse not to use them to inform your key metrics, and then to monitor your business based on those dimensions of user experience, functionality, and performance.

Intelligent testing 101: A typical use case

A company had an e-commerce target to sell 10 million dollars’ worth of merchandise, but they were only achieving five million. Via the close analysis of the system, the company noticed that it was the millennials that were not buying, which gave the analytics team the ability to focus on particular views of the user journey.

Using intelligent testing, the company automatically came up with a model of the system by analysing it, which allowed it to build tests from this model to pinpoint the exact areas causing the disconnect for millennial users.

It turned out that the responsiveness of the site was more than three seconds and at times up to 10 seconds before a user arrived at their basket, which caused people (millennials in particular) to get frustrated and move on elsewhere before completing purchases. Needless to say, gaining this intelligence enabled the companies to fix some key performance issues which were affecting the responsiveness of the site, allowing them to get all users to a view of their basket quicker so that purchases were not abandoned.

Intelligent and continuous testing

As the use case points out, it’s essential that companies take user experience and the user interfaces seriously: it’s got to be about looking at the user interface, understanding what all the elements of the application are and how they are feeding together, and how people are using it. It’s only then that companies can really deploy intelligent testing and automatically generate and prioritise the right tests from it.

When all working parts are in order, intelligent testing can actually build models by itself to watch and understand the system continuously. Different aspects of AI can look at the interface and identify all the different parts of a workflow, seeing how people use the system to understand and explore the unanticipated.

The machine learning aspect is critical for developing a testing model that can continually build itself by monitoring and understanding the system it is applied to. Because AI identifies all the different parts of a workflow, it can see how differing applications use the system on a regular basis and update its learning model to anticipate how new user devices and behaviour patterns could disrupt the website.

What will intelligent testing look like by the end of 2018?

By the end of this year, forward-thinking companies will be knee deep in learning feedback loops, analysing testing results to see what worked and what didn’t, while also thoroughly analysing feedback from continuous testing and monitoring that actively shows where real users run into problems. Intelligent testing will give companies the power to zoom in on problems quickly and fix them, providing remedies and a solid strategy to test with the means to continuously improve the business.

By the end of this year, those that will win will have grasped that the results of the testing process hold far more value and insight if AI and machine learning driven. Testing has become more intelligent than ever before, and can and will become a key business accelerator for those businesses who choose to nurture it.

Written by John Bates, CEO, Testplant