AI in Test Automation: Opportunities and Limitations

Opening AI to The World

There is no going back; Artificial Intelligence seems to be here to stay with us, for good: we are kind of doomed to make it work –also for good.

From Alan Turing’s 1950 paper on thinking machines and the first experiments at Dartmouth College in the mid-50s, to AIML chatbots, between late 90s and early 2000, Artificial Intelligence has been going through a fast paced maturation into what is known today.

Though a lot of water has passed under the bridge concerning Artificial Intelligence, it was not until the early 2000s with the advances made in the field of Machine Learning, seen from the potential it has today, that it really took off.

Managing to make machines capable of making predictions and achieving a certain goal based on the acquired and processed input data, or, in the case of unsupervised learning, making decisions based on pattern recognition of data gathered from their external environment, is a true milestone of computer science.

Realistic Applicability

But although research in AI has already explored many roads in terms of applicability, only some of them so far have shown reliability and practical utility.

As much as many would like to own self-driving cars or have robotaxis working in their cities –especially those who can’t drive or don’t own a car, these are technologies which are mostly considered to be still in their testing phase. Many concerns arise regarding the use of this technology in the civilian sphere, particularly in matters of safety, which are of significant public interest. These are questions not to be taken lightly at all if we want innovation to have a positive impact on our lives.

However, there are other contexts in which AI has indeed successfully been applied, and is actually thriving, and that is the software industry. Virtual assistants, chatbots, translators, speech recognition and text elaboration are just some of the examples in which AI is being implemented.

But one of those way in which the implementation of AI has been of utmost sucess, is automation testing.

From Newborn Steps to Maturity

Although test automation has been with us since around the mid-1980s, the inevitable boom of computer software did not take place until the turn of this century, which brought with it the need for better production quality standards and, eventually, the wonder of modern, full-code test automation frameworks.

While they did not become popular until the mid-2000s, full-code test automation frameworks (the likes of what the best options on the market tell us) really helped to change the game of software testing, by accelerating the testing process and therefore, production and time to market. There is a lot one can really do with technologies like these if their potential can be harnessed the right way.

But what does it take, or what does it mean to use them the right way?

Automation Frameworks: Costs vs. Benefits

No one will argue at this point what full-code automation frameworks can deliver or how well they do it. Once a test framework is properly set up and its test code intelligently written, it is as strong and reliable as a tank. But even tanks require servicing from time to time. So there is one thing that remains constant since the mid-1980s till today; in spite of all the ups and downs they have gone through, what used to be true for automation tools then, is still true for full-code automation frameworks in the present: test scripts need maintenance performed on them whenever the tested application codebase changes. This has a huge significance today, in a world where practically everything, from eCommerce stores to public services, depends on software to run.

Massive demand for goods, creates the need for faster delivery times, which at the same time causes teams to adopt tight deadlines on their projects, the kind of tense situation in which any error, no matter how small, can represent a truly quantifiable loss.

Added to this is the cost of staff training or hiring, because, let’s face it: creating well written test scripts is not something any person can do, and even being reasonably good at that, doing script maintenance is a painstaking and time consuming task; time that could much better be invested in testing per se.

AI and Test Automation: The Revolution Taking Place.

It is true what they say; Artificial Intelligence is pretty much all that. Machine Learning algorithms tailor web search results, movie suggestions, home page content in social media, etc, all according to the user likes, preferences and/or actions. We still seem to be not amazed enough at how Large Language Models (LLM) can read and (reasonably well) write poetry.

In sum, the power predict or “infer” possible states of an entity, as well as the ability to perceive changes at a visual or graphical level, are all benefits which Automation Testing tools have taken great advantage of.

Today, no-code Automation Testing tools –especially those based on AI– are helping testers to save a lot of time and effort in tasks that otherwise would take –most of the times– massive lines of code (and sleepless nights), like finding and defining element locators, even when the codebase is changed. Plus, applications or browser extensions for recording and playback are a real kicker in simplifying the way testers can create test scenarios or assertion steps for elements or their respective states on a user interface.

Not The End of The Road

But in spite of all these good news, AI, like coded automation tools –and basically all technologies–, still have things to perfect, and areas to explore.

This is beyond the fact that machines can not think like a human being. It’s more like, machines are conceived and designed to do specific things, and, desirably, to do them well, fast and in a constant pace; in contrast to the randomness that human error adds, which is often necessary to come up with, for example, interesting or unpredictable test case scenarios, steps or ways to reproduce an error, etc.

Artificial Intelligence as a research field is undoubtedly pointing and heading towards that goal: machines that can reason, machines able to make conscious decisions, likely how a human being would proceed.

In the coming years, new discoveries made sub-fields like Machine Learning, Deep Learning and Convolutional Neural Networks will probably yield their fruit and give birth to the new thinking machines.

On the Edge of Tech: Autify Delivers Quality

Even though we are still not there, living that promising future which lies within imagination, hope and certainty, there are for sure important milestones reached so far; and Autify is certainly aware of that, being a contributor in making that future possible.

By improving and adding to the already existing features of its platform, Autify maintains the clear vision: helping QA teams make the best of their resources, accelerating training phases, while giving non-programmers the opportunity to participate in the tech industry, directly involved in the development process of a product.

Autify simplifies the test case process creation, leaving free space for the tester’s ingenuity to generate better, more creative test scenarios and test cases, leaving the hassle of dealing with codebase changes and test maintenance to the very core of the system, making testing the motor of the whole development process.

With its latest additions, like Step Suggestions, Autify demonstrates how it is pushing the envelope in harnessing the power of Artificial Intelligence, without leaving aside the fact that people very often like to get their hands dirty, and there are indeed testers who are familiarized with programming and can actually code; that is why Autify’s JS Steps and JS Snippets are a very important part of the whole Autify application, which extends the capabilities and the possibilities of it.

Conclusion

The future is nigh, because it is always ahead, and so, ahead is the way to go. And that means, overcoming limitations, finding new ways of solving old problems –while dealing with the new– and reinventing oneself.

Autify embraces that philosophy and encourages the new generations of QA testers to be part in building that future; a future in which automation is no longer a thing to be doubted about because of the difficulties (and costs) it might represent.

Autify is for you if:

• The development team wants to run tests first.
• The QA team tests multiple products across the board.
• The QA team is short staffed.
• Test automation seems too difficult.
• You have struggled with test automation before.
• You haven’t got around to maintaining test codes.

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