Everest Reinsurance

Artificial intelligence is all around us, whether we recognize it or not. From your smart assistant on your phone to the Netflix recommendation that keeps you binge-watching, we are continually engaging with Artificial Intelligence in our everyday life. Intelligent chatbots are slowly taking over level 1 interaction instead of human customer support personnel. Grading at schools is becoming easier, fair, and more accurate, thanks to AI-led plagiarism checks. Email spam filtering and smart response to email/messages are slowly becoming part of how we use these tools. Artificial intelligence is now helping healthcare practitioners diagnose the disease earlier and more accurately. In many cases, AI models are faster, and more accurate and successful algorithms have been created for analyzing cancer cells in biopsy reports and x-ray reports.

It is without a doubt that Artificial intelligence is helping ordinary consumers in our everyday life, but the big question is what is the impact going to be on the jobs that were previously done by humans and especially of concern are the manufacturing and technology workers whose careers seem to be the most vulnerable.

This article will focus mainly on Software quality assurance/ Testing jobs and broadly on other technology workers (Software developers, support professionals, customer support representatives, and even project managers).

AI journey:

Before we understand the impact of artificial intelligence on Quality Assurance jobs, we need to know where we are in the AI journey. Regardless of how much hype there is around AI these days, AI is not a new concept. Ancient Greeks had myths about robots, while Chinese and Egyptians built automatons. However, modern AI finds its roots in the 1950s. The term “Artificial Intelligence” was coined in a conference at Dartmouth College in Hanover, New Hampshire, in 1956. British Mathematician and computer scientist created a test to assess if a machine is more intelligent than humans, and this test was most famously known as the Turing Test. We have seen many AI winters (periods where funding, research, and interest in AI dropped) since then, and here we are talking about it again that AI is ready to take over the world.

There is no standard way of measuring how far we have come along in terms of AI but here are rather simple stages of AI for our discussion-

• Artificial Narrow Intelligence (ANI)

• Artificial General Intelligence (AGI)

• Artificial Super Intelligence (ASI)

ANI is also called “Weak AI” is an AI that is programmed to perform a single task. They can perform a task in real-time, but they still work a specific data-set. Whether it is your smart speaker, weather app, or AI used in credit card fraud detection, they all are examples of Artificial Narrow Intelligence.

 Technology professionals will have to be proactive and invest in learning to be relevant and not only survive but thrive in the future 

I will not delve deeper into AGI and ASI but only mention that AGI is a stage where AI can perform tasks that a normal human being is capable of. This type of AI is conscious, which weak AI is not. When it comes to ASI, it is a stage where AI will hypothetically surpass all human intelligence in all aspects, and we reach a stage called “Singularity” as per futurist “Ray Kurzweil.”

But all types of AI that we see around us are still Narrow AI or Weak AI, so we are still scratching the surface. There is much progress made, but an enormous amount of work is yet to be done for humankind to reach anywhere close to AGI and ASI, the kind of AI Elon Musk is worried about.

What is the impact on Software Quality Assurance:

Let’s come back to our topic on how do the advancements in AI affect jobs and, in particular, Quality Assurance jobs. Let me answer in two ways:

One- No, it is not very far in the future. AI and machine learning are already made its way into all aspects of tasks being performed by Testers and other technology workers. The software development process is evolving, and some tools have been helping software developers and QA professionals to predict the likelihood of defects, do automatic error handling, code refactoring, automatic test script creation, execution, and reporting defects.

Two- AI is not a monster that will take away these jobs, but if we plan it right it can be an enabler of creating more jobs and make lives of technology workers easier by taking away mundane manual tasks away from them while having them focus on more value-adding activities like user experience and further advancements of technology.

Being a technology profession myself, I will be focusing a lot on what we can do to keep ourselves relevant along with AI in the future.

As per the world economic forum, automation will displace 75 Million jobs by 2022; however, it will also create 133 million jobs, so a net addition of 58 million new jobs. How is that possible?

In my opinion, three things will happen:

1. Some jobs/ tasks will be eliminated entirely

2. Some jobs/ tasks will be transformed

3. Brand New jobs/ roles will be created (Thanks to AI)

Here are more details on trends and what I think will happen in terms of QA jobs in the next few years:

QA jobs that will be eliminated or greatly reduced:

• Manual testing: I don’t expect it to go away totally but it will be greatly reduced with AI incrementally doing more and more that was traditionally done manually.

• Manual Review: A lot of manual testing artifacts like test cases are reviewed manually, and this is a significant portion of what test leads and test managers spend their time on. I expect AI tools to take over some of these tasks at least partially very soon.

• Manual Estimation: Estimation for testing efforts continues to be a manual effort despite the use of tools for test management. This creates bottlenecks in Agile projects where things need to move quickly. I expect the use of AI tools to estimate the time required for testing quickly.

• Test Status Reporting: This is a necessary evil, and despite not many people reading these reports they have to be sent out. Much manual effort is required to create these status report and I expect that AI tools should be able to send reports and show intelligently what changed to the right stakeholders. There is much scope in this area not only for QA but the overall tech industry.

• Manual Documentation

• Repetition of Test in multiple browsers, device or environments