Intelligent Automation - Machine Learning


John Bates the CEO of Testplant has exactly my views on what smart and intelligent test automation would be:

Quick snippets:

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”).

2. Intelligent test coverage generation and ‘bug-hunting’ - There are a potentially infinite number of paths through a complex app so which ones should we follow in our automation? We can use AI classification algorithms such as Bayesian networks, to select paths and 'bug hunt'. As these paths are explored, the bug-hunting AI algorithm continues to learn from correlations in data to refine the coverage and help developers identify root causes and fix defects.

Through a combination of bug hunting and coverage algorithms, AI and analytics will exponentially increase coverage and productivity. AI algorithms will hunt for defects in applications based on user journeys automatically generated from this bug-hunting model, while coverage algorithms will select the user journey that is the furthest away from others that have already been executed. Non-instance-based learning algorithms also reduce the amount of 'learning' required giving quick results, essential in Agile and DevOps environments. Ensuring that the algorithms are delivering defects and coverage is a balancing act, and where AI will need input from a smart tester who knows the system well and can dynamically adjust the trade-off between coverage and bug-hunting.

3. Continuous test, continuous learning, predictive trends - Testing digital apps is not just a ‘one and done’ exercise. It should be a continuous process - so that we are essentially monitoring the digital experience over time. And an AI algorithm should be watching the test results over time - learning and looking for trends. These learning algorithms can then build decision trees that enable predictive analytics to identify, for example, if based on experience, the increasing delay on a particular workflow, we’re heading for a system outage. We could address this before it becomes critical and causes customer outrage.

Can AI Transform Application Testing?

According to app store giants Google and Apple, 80% of downloaded apps are only used once -- and a whopping 96% aren't used after the first month. With such meager success rates for new apps, Dr. John Bates, CEO at TestPlant, explores the question: Can AI transform app testing - and bring us all better results?

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