The AI revolution: Software testing's new best friend

Artificial intelligence will transform software testing for the better. AI algorithms will evaluate systems to auto-generate previously resource-intensive scripts, analyze the results to predict bugs, and adjust the scripts to improve test coverage.

This will be a huge change from the testing environment of just a decade ago. Then, software testing was perceived as a compliance activity to ensure that code worked. It was a murky endeavor that was kept locked away in the IT closet and received little attention from anyone.

Now every organization is racing towards digital transformation. A digital experience that delights customers is no longer a nice-to-have; it's essential for survival. But in a hyper-connected and complex world, this is no simple task.

The software world has undergone a revolution as a result of the cloud, consumerization, DevOps, microservices, and the Internet of Things (IoT). Today, apps and software releases are being rolled out continuously and at a far greater pace, coupled with billions of devices being digitalized and connected to the cloud.

In this diverse and fragmented technology world, the traditional, manual approach to testing has no hope of keeping up; manual testing is rapidly facing extinction, so a new approach to testing is essential for survival in a digital world. This is where AI can help.  

The AI way

Ultimately AI, machine learning, and analytics will act as the catalysts to deliver true test automation—recommending the tests to carry out, learning continuously, intelligently predicting business impacts, and helping development teams fix issues before they occur.

Here's how will this testing nirvana will evolve.

1. Intelligent automation

The only way to truly test a digital app or software from the user perspective is by way of an intelligent automation engine. This system accesses 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 requires technology that understands 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? You can use AI classification algorithms, such as Bayesian networks, to select paths and bug hunt. As you explore these paths, 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 that are automatically generated from this bug-hunting model. 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, which are essential in agile and DevOps environments. Ensuring that the algorithms are delivering both defects and coverage is a balancing act, and 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 testing, continuous learning, and predictive trends

Testing digital apps is not a one-and-done exercise. It should be a continuous process, so that you're monitoring the digital experience over time. An AI algorithm should continually watch your test results over time, learning and looking for trends.

The learning algorithms can then build decision trees that enable predictive analytics—and can identify, for example, if, based on experience, the increasing delay in a particular workflow is likely to result in a system outage. Armed with this information, you can proactively address it before the problem becomes critical and generates a customer outrage.

What it all means for you

So what does this mean for test teams? The role of the tester will become more strategic. Test engineers will guide the testing process and play a key role in deciding what makes software and apps delight the end user. With true test automation, the productivity of the human tester will increase significantly.

But one note of caution: Manual testing models that rely on vast teams of humans based in lower-cost markets do have something to fear. 

AI is the new BFF of software testing, as it is the key to teams keeping up with the pace of DevOps and producing higher-quality software and apps. This will positively affect customer conversion, retention, and ultimately revenue generation.