Can AI Transform Application Testing? Posted on : Sep 08 - 2017

According to app store giants Google and Apple, 80% of downloaded apps are only used once and 96% aren’t used after the first month. To look for the culprit for such disappointing results, one need only look for the strong (if not direct) correlation between user experience, retention, revenue, and app performance.

So, in this light, no wonder that app testing is taking on new importance. Testing is no longer just about “does it work as specified?” Today, testing is becoming about “will it delight customers” and provide a high quality, performant and usable ‘digital experience’? This shift means digital teams are currently struggling to deliver high-quality digital experiences that delight users in a way that’s fast and engaging.

 Many experts today say that artificial intelligence (AI) is in a position to fundamentally change the way we work and live. In fact, during Google's I/O conference last May, the company stated that we already live in an AI-first world -- and no one can escape the hype.

 So, the question naturally arises: What might AI mean for how we develop and deliver software and applications?  Can AI actually improve the odds of building a successful (and delightful) app?

 It turns out, in testing and monitoring of the digital experience, AI and analytics can be critical – but perhaps not in the way you may be thinking.

There’s a lot of talk currently about test automation. However, in reality, we’ve only automated one key element: test execution. AI and analytics will be the catalysts to deliver true test automation that recommends the tests to carry out, learns continuously, enabling it to predict business impacts, and enabling dev teams to fix issues before they occur.

 So, here are three ways we see AI is enabling this new type of ‘predictive’ testing:

 1. Intelligent automation - The only way to realistically test a digital app is through an intelligent automation engine accessing the application as a user would - taking control of a machine, actually using the app to exercise workflows and collecting intelligent analytics along the way. This involves technology to understand on-screen images and text, such as smart image search and dynamic neural networks (so called “deep learning”). View More