Notions, Facts and Figures

It has been said time and again that emerging, disruptive technologies like Artificial Intelligence and Machine Learning would flood the tech industry and somehow or somewhat erode the job market, etc, etc.

Even though it is true that Artificial Intelligence has made a big entrance in the industry and is playing an important role in the show, and without pretending to pass by the ethical and social implications which it presupposes (that is, governing, policying and regulating it), none of those predictions have so far come to fruition.

David Autor, an economist at the Massachusetts Institute of Technology was quoted in one very interesting article from 2016 published on The Economist, stating the following:

«(…)in the past technology has always ended up creating more jobs than it destroys. That is because of the way automation works in practice. Automating a particular task, so that it can be done more quickly or cheaply, increases the demand for human workers to do the other tasks around it that have not been automated.»

By utilizing natural language processing (NLP) and innovative technologies like Generative Artificial Intelligence, knowledge graphs, and composite Artificial Intelligence, corporations are progressively employing Artificial Intelligence solutions to develop novel products, enhance existing products, and extend their consumer base.

Two studies from Gartner, Inc., one from 2019, stated that organizations that decided to implement Artificial Intelligence grew from 4% to 14% between 2018 and 2019, while the other study, from 2021, ventured to make some interesting projections for the year 2025:

  • 70% of organizations would have operationalized Artificial Intelligence architectures, given how fast enterprises have been gaining insight into how to organize and control Artificial Intelligence.
  • Generative Artificial Intelligence techniques will systematically discover more than 30% of new drugs and materials.
  • 70% of companies will have to shift their attention from large to wide and diverse data sets (Big vs. Small data). This approach giving more context for analytics while making Artificial Intelligence less data-intensive.

It follows from these projections that there is a significant amount of hopes put into Artificial Intelligence. A graph chart extracted from the same Gartner, Inc. 2021 study expands some more on that:

Gartner Artificial Intelligence Hype Cycle for 2021 describes AI-specific innovations that are in various phases of maturation, adoption and hype.

All this should be enough to remind us the potential challenges that Artificial Intelligence poses to society in general, and the tech industry in particular. But when we talk about the tech industry, that is, the Information Technology industry, there are however some niches in which we can safely affirm that Artificial Intelligence is an indisputable winner. Automated testing of computer software is definitely one of those niches.

The Ideal Blend

If programming a machine or a software application to become an autonomous agent is indeed to create an Artificial Intelligence, then by definition it is a kind of automation. It is another level of automation.

What then would not be the possibilities of automating automation? It may sound silly or redundant, or quirky. Or, it could all sound a lot like exponentiation, much like a modern electric bicycle if we think about the old Steve Jobs’ Bicycle for the Mind analogy.

So there they are: two concepts –Test Automation and Artificial Intelligence– born from the cutting edge of technology, blended into one, at the service of the living, thinking human being.

And What is with Automating Automation?

Still to this day, coded or full-code automation testing frameworks coexist with low or no-code testing platforms.

There are excellent options in the market for coded solutions, be them proprietary software or open source software; each of them with their respective advantages and downsides. However, they all coincide in one key aspect: they require a programmer to design test cases, test suites and test plans, besides coding and setting up a whole test framework, and most important: performing test maintenance.

Hours and hours of precious time and valuable brain energy are invested (to not say wasted) day by day in such a set of repetitive, intellectually unprofitable tasks. And this is precisely the matter.

In the same 2016 article from The Economist mentioned above, David Autor is quoted again saying:

«(…)Focusing only on what is lost misses a central economic mechanism by which automation affects the demand for labour: that it raises the value of the tasks that can be done only by humans.»

Now pay good attention to the graph below, because it is saying exactly that in some way.

Tasks that can be done only by humans. Think about that for a moment.

Humans can no doubt perform repetitive, mechanical tasks such as typing, entering records into a database, running batch jobs on certain software platform, etc. But machines, like it or not, can outperform humans at those, and generally all heavy or intense work. A machine will not get tired or confused, nor ask for coffee and donuts, and will do exactly as instructed as long as it is powered.

Artificial Intelligence technology is being increasingly utilized for creative tasks. A 2023 survey by Statista, involving 4,500 US professionals, revealed that 37% of the respondents working in marketing or advertising had utilized AI to support their work duties.

So where do the human qualities fit in all this?

Creativity and Meaning

The 2023 Future of Jobs Report, published by the World Economic Forum (WEF), indicates that the essential competencies for workers in 2023 are two particular cognitive skills: analytical and creative thinking. The WEF report demonstrates that creative thinking is gaining prominence compared to analytical thinking.

The Oxford dictionary defines creativity as: “the use of imagination or original ideas to create something.”. There we stumbled upon something interesting.

Imagination, desire, volition, as well as creativity or the ability to extract abstract meaning from something –work, art, etc., are all faculties and phenomena unique to human beings. A machine can not desire to perform some task nor imagine how to; it will do whatever it is designed to do, according to specifically set parameters, no matter how many statistical or stochastic calculations it may be able to do compared to a human.

This then brings us to the importance of meaning, since meaning is what ultimately gives value to one’s work. And given the fact that most humans desire to do meaningful work, generating or facilitating a space for creativity is therefore a very relevant issue.

A very interesting study from 2015 defines meaningful work as happening “when an individual perceives an authentic connection between their work and a broader transcendent life purpose beyond the self.”.

If our work feels meaningful to us, we become more engaged, committed, and satisfied.

Machines that Save Creativity

Testers wanna test, should be the motto of all QA workers when it comes to automation. If pressed, most would say they would much better like to the job without actually having to do all the preparation work it implies. This is totally related to what we have talked about in the last section of this article.

Artificial Intelligence can then give us the best of both worlds, without making us feel like we are in any sense displaced.

Today, advances in the sub-field of Artificial Intelligence, Machine Learning, allow automation platforms like Autify to learn about the codebase of an application and its User Interface components, perceive changes in them and make the appropriate decisions in spite of those changes. This is absolutely helpful when test maintenance is needed or when performing Visual Regression tests.

Also, until now, Autify has rid the tester from writing test scripts, thanks to its Autify Recorder Chrome extension, which lets the tester create test scenarios, freely, only focusing on designing the best possible test cases. That is a huge step towards giving value to human work.

However, if time is a constraint, now the tester can make use of Autify’s recently implemented Step Suggestions Chrome extension, which takes advantage of the popular Large Language Model (LLG) chatbot, ChatGPT (GPT-4), to aid the tester in deciding test steps when recording scenarios.

So this is, roughly put, what the machine can do. We are still interested in how the human is empowered to give value (and so, meaning) to his own work.

Owning your Own

What can the human tester do, now that he is free from executing the boring stuff?

He can unleash his creativity, by thinking about the most possible use cases –from a human (customer) point of view, and employing his own heuristics into designing the test cases which may cover all the thinkable (or unthinkable) paths, giving value to his own time and intellectual resources, thus claiming his own position as what it is: a software tester.

He will not only gain value for his own work; he will give value to his team, to the project he might working on, and ultimately, to the customer, which is the end goal.

Autify provides that possibility for everyone who uses it.


Artificial Intelligence has opened a whole new scope of possibilities for the application of computing technology; that is an undeniable fact. What we do with or of it, it is our business to take care of it.

In the meantime, what can be said is that Artificial Intelligence is helping to alleviate workers from unnecessary boring bulk of the daily routines, giving a breath of fresh air to creativity and self-motivation.

Autify stands at the forefront of technology, waiting for human beings with the desire to unleash their innate powers.

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