Is the New-Age Quality Engineer a Cyborg?

Thank you so much. May I now request the Founder and CEO of TalentSprint, Dr. Santanu Paul, to please take the dais and give us some keynotes about his speech. He is a visionary leader and an entrepreneur who has revolutionized the way individuals and organizations approach talent development. He established TalentSprint, a global platform for deep tech education and high-end coding boot camps, which has garnered investments from prominent players like Nexus Venture Partners, the National Skill Development Corporation, and the National Stock Exchange Group.

Under his leadership, TalentSprint was honored as a Training Partner of the Year at the prestigious PEGA World Inspired event held in Las Vegas. Before TalentSprint, Santanu held senior leadership positions in leading technology companies. He served as a Senior Vice President and a Global Director of Operations for Virtusa Corporation. He also led CTO positions at OpenPages and Viveca Inc., both Boston-based tech firms backed by notable venture capital firms like Matrix Partners and Sigma Partners. His diverse and impactful career reflects his passion for innovation, talent development, and driving positive change in the technology landscape. Sir, your address to the guests here would be highly appreciated.

Just on that note, I’m very honored to say that I’m working for Virtusa now, and it’s very good to see you in India. Over to you, sir. Thank you very much, Madhu, for this opportunity and the rest of the team that invited me here.

When Madhu called me, I think a month ago, and said, you know, you should do this keynote, I asked him, like, you know, I’m not a testing person. But then he said, you know, it doesn’t really matter. We want you to make people think. So he gave me an interesting challenge. And my task today is to live up to the challenge of seeing if I can make you think.

Of course, I’m trained as a software architect. So in some sense, while I’m not a testing professional, I’m certainly highly interested in quality. And I think one of the major things I spent a lot of my life on, especially as a technologist in my pre-entrepreneurial incarnation, was to really worry about software quality in a very, very deep and great way.

So today I thought I’ll, you know, sort of get going on a topic which, you know, many of you here may or may not know. But how many of you know what a cyborg is? No? Take a shot. Okay. Terminator, T-800. Huh? Yes. Okay, so if I take that, what Lakshmi is saying, that it’s human combined with technology, machines, electronics, and all of that, right? Is that okay? So we’ll start with that, right? So let’s begin with this idea that we’re going to talk about cyborgs. And the way I’m going to talk about this presentation is going to break it up into three parts.

The world of cyborgs. A world of escalating software complexity, which I’m sure all of us are very familiar with. And then finally, what are the characteristics of a new-age quality engineer? And what are the fundamental principles of new-age quality engineering?

So those are the three parts that I’m going to get into.

Let’s start with the world of cyborgs, right? I think we just started with Terminator, so right there we have Terminator. Good, well done. So you have Darth Vader from Star Wars. People remember Darth Vader from Star Wars? Yeah? Okay, very good. So what was Darth Vader? He was basically a cybernetic augmentation. He used to be a real person but then became, over time, infused with technology. With various accidents and other kinds of infusions, he got cybernetic arms and legs, which means that he became part machine, part human.

So that’s Darth Vader in popular entertainment fiction. Then you have Terminator, T-800, popularly played by Arnold Schwarzenegger in all the movies and the Terminator series. And here, the whole idea is that it’s doing good. It initially begins with the idea that it’s all machines and humans used to do damage. But really what this is, if you think about how it is described, is that Terminator is like an endoskeleton, meaning the inside of the body is machines, but the outside is human, flesh, and blood.

So you have, again, an example of a human-machine integration that’s doing things in the real world, where the boundaries between technology and human are not very clear. As the lines blur in all of us, the whole idea is that we are going to essentially be in an age of cyborgs, where humans and machines are going to have a very blurred boundary between the two entities.

The third, of course, is Tony Stark and Iron Man, and this is a Marvel series. Here, Tony Stark is the character, played by Robert Downey Jr., who’s a very famous and popular movie actor. I’m sure many of you have seen his movies. Okay, Tony Stark says that the brain and the spine that I have are human, the rest of me is machines. Correct? So the most important part of who we are is our ability to think, our intellectual ability. So the brain and the spine are human, the rest of it is machine.

Again, a human-machine interaction. So that’s real life, as we say, in entertainment.

But let’s look at the real-life world, right? In real life, too, things have begun to happen. It is no longer just a world of fiction that we are in. We are in the world of reality. Let’s start with Stephen Hawking.

People have seen Stephen Hawking? Yes. After he had this disease called Lou Gehrig’s disease, or ALS, in his childhood, his college days, he essentially became dependent upon computers and his wheelchair to speak, to move, to do everything. And he had a very successful intellectual career.

You know, he’s one of the most famous physicists of our time. He had a family, he had kids, he had everything. He had a proper life. But essentially, he was a human brain and a very frail body attached to a very complex set of machines, but highly functional, right? Not easy to be a world-class physicist, but the cyborg Stephen Hawking achieved that.

Then we have an example of lesser-known people, but this is a very interesting set of people you can Google, if you wish. A guy called Neil Harbison essentially got himself implanted with an antenna in his brain. It’s actually surgically implanted into his brain. It took a lot of interesting surgeons to do this work for him. And what it does for him—he’s an artist, and actually, you know, I think he’s an artist-musician, but primarily he calls himself a cyborg artist—is using the antenna to improve his perception of colors. So if you Google him and read the Wikipedia on this guy, it’s very interesting. He’s basically saying that the future of art will be artists who are part human, part machine. And he’s a real person. So look it up.

And then the final one is a Canadian filmmaker named Robert Spence. He had an accident, an eye injury, and the doctors could not save his right eye, so he kind of lost his eye. He ultimately had one eye, the left eye. But he had this idea that, as a documentary filmmaker, when you take a camera and go around, for example, like a lot of cameras in this room, typically when you put a camera in front of subjects, they become self-conscious, right? We become a little careful, we don’t say what we want to say, we might be more stiff, and we have all kinds of problems in front of the camera. So the camera distorts the subject.

So he said, why don’t I use my damaged right eye socket to implant a camera, a proper video camera there? He walks around, talks to people, and essentially he’s recording. So he is now no longer, and his eyes are well-matched, so essentially what he is doing is going around, talking to people, and continuing his work of documentary filmmaking, but getting source material, of course, with permission. But people are not conscious of the fact that he’s recording them, because of the way he’s using his camera.

So anyway, interesting topics to discuss, and I’ll let you all reflect on this a little bit, to say that the idea that humans are going to become surrounded by machines, whether hardware, software, or both, is no longer a very strange idea. It is actually a very natural idea; it’s becoming more and more common.

Alright, so let’s move on. Now this, I’m going to take it to the next level, right? I’m going to talk about what is really happening. In China today, firefighters—people who go out and fight fires, you know, fire department professionals—get to wear these firefighting exoskeletons. It is really something like a suit, but when you wear it, you’re augmented with all kinds of facilities. One, you can see inside smoke, you can have physical power that is two or three times your normal power, so you can move things around, right? You have extra camera power, so you can see through difficult situations, and you have the ability to run faster because the legs of the suit are designed for faster mobility. So all of a sudden, you are a super-powered, superhuman, or superwoman firefighter by wearing the suit. This is an exoskeleton, so the moment you’re inside it, your ability as a firefighter just goes up 5x or 10x, yeah? So now, while you’re inside the suit, where the human ends and where the machine begins is not very clear, because now you are embedded, the human is embedded inside this machine that is making you a better firefighter.

So this is happening, nothing new. This is even more interesting. This is happening in Hyderabad as we speak, right? There are surgeons in the city who are using robotic platforms. You might have heard of Da Vinci as a robotic platform, where, if you look at this picture, the surgeon is on the left side, correct? Here on the left side, the surgeon is essentially looking into a big console, her head is buried inside this machine, and she’s looking at the patient who’s on the other side, on the right side. The cameras are trained on the patient, and the robotic arms are all positioned on the patient, right? Now you have dozens of arms in the robotic surgical machine. The surgery is happening; the robotic arms are doing the surgery, but the surgeon is on the left side, essentially working with the robotic arms. So for one practical purpose, you can say that the surgeon’s arms are extended into the robotic arms, right? From the perspective of the surgeon, you are now multi-armed, like Goddess Durga. You have ten arms, let’s say, and you’re able to do something that you cannot possibly do with your bare hands.

In fact, I interviewed one of these surgeons a few years back, and he said to me, I asked him, at what point did you decide to become a robotic-assisted surgeon? He said, the day I realized that I’m getting older, and the day I realized that no matter how good I am, I can never rotate my wrist 180 degrees. Correct? Have you ever tried to rotate your wrist 180 degrees? You can’t do it. So surgeons would like to do that, because two things happen when you get surgeons in a robotic platform.

Patients have better outcomes, because the amount of blood loss, cutting, etc., goes down. But on the other hand, the surgeon also has less stress. The surgeon’s getting better outcomes, because the performance is better, and they’re getting better results.

So it turns out that by using robotic surgery, a surgeon can extend their career by ten more years. So if a regular surgeon can work for 30, 40 years, because after a certain point, your body won’t let you, with using machines, you can have an extra ten years of career, productive career. So all of a sudden, the professional and the subject, the patient in this case, are all benefiting from robotic surgery.

So again, an example of a cyborg. My last example of cyborgs, pilot cyborgs. So what is this? Imagine the amount of money it takes to train fighter pilots. Enormous. If you have to have human trainers, who are pilots, training other pilots, to fly, you know, expensive combat aircraft, that’s really difficult. So what you want to do is you want to train machines.

So today what you have is, when the flights take off, training pilots, all the data is being collected by this machine, the computer systems, they’re being fed into a simulator, which is a simulator you see, this particular training pilot. So this pilot has gone off, flown some time, come back, the machine now knows what they have done well, what they have not done well. What they have not done well has turned into training plans. The simulator is now pushing the pilot to deal with those training plans. Correct? So you build a feedback loop without humans in the loop. The machine is essentially training the pilots, next generation of fighter pilots.

So I could go on for many such examples, but I’ll stop there as part of the first part, for me to convince you, hopefully, that the future that professionals, and I’m talking about professionals, if you are a professional in any field, the idea of augmenting yourself or enhancing yourself using great technology and making the technology an inherent part of your capabilities is actually very good for you, because that’s exactly how you become a super professional. That’s point one. The question that applies, of course, is does it really apply to quality engineering? We’ll see more about that.

So let’s look at the world of software complexity. What are we dealing with? There are spectacular failures and spectacular successes in quality, right? One failure we all know, you know, Boeing 737 MAX, you’re aware of this problem. Have you seen the documentary in Netflix? Okay.

So this was the particular one, the MCAS system, essentially caused two major crashes over the last three, four years, and this was a new plane that had been rolled out under a great hurry because of business pressures, and it had two fatal crashes. Initially, Boeing refused to accept it, and then finally they had to accept that there was fundamental flaws in the software. Of course, it’s not a new problem in quality and testing. We’ve been seeing this for 50, 60 years, that simple errors can cause life-threatening outcomes, and this was one of them. But the interesting thing is that if you read the article in the print, there’s a lot of blame put on Indian software professionals, saying that there are companies that I won’t name, two, three very well-known companies out of our own community of companies in India, but specifically blamed for producing software that is a poor quality. Now, of course, some of this is just people saving their skin, but the point remains that complexity is high when you have these airplanes, which are essentially nothing but airplanes which are souped up with lots of technology. They’re essentially automated to fly under conditions where the pilot doesn’t have to get involved. Pilots are incentivized to use the automated flight pilot mode as opposed to manual flight pilot mode for other reasons, like saving fuel, for example. So there are a lot of things that are happening in the world of aviation where it is going to be more automated flying. Pilots are going to be more Borg-like, as we speak, and that also leads to the question of saying that how do we test software or ensure quality of software under such difficult conditions? Another big failure, Tesla’s crash a few years back, which led to lawsuits. Tesla finally won the lawsuit, but the idea was that, of course we know that computer vision has reached a level where it can do a better job of recognizing obstacles in the road than humans, because humans have inherent limitations. There’s a 3-4% error rate humans will always have in making a judgment, but when you have computers doing it, you do a better job, but finally when a crash does happen like this, who’s liable for it? So there is problems of, the moment you get into autonomous driving, you’re going to have this problem. We have to start worrying about how do we ensure quality in a complex environment. Let me give you some success stories. Chandrayaan, right? After Chandrayaan 2, Chandrayaan 3, the entire model was changed to say that if you assume that failure is normal, failure is what is normal, and success is an exception. It’s highly defensive quality engineering. Then they built a model of saying that, okay, what if you assume that anything and everything can fail at any and every time? And you build your entire model of, starting from the architecture, the design, to the entire test plans, you work with this approach that anything and everything will and can fail. So therefore, the success of Chandrayaan 3, to a large extent, was because of the enormous number of simulations that were run under enormous number of conditions, well before the flight server actually took off. So that’s a good example where quality engineering has successfully done something that nobody has done before. The last one, which I’ll give you an example of, which all of you are familiar with, and I had a five-year stint with NPCI when UPI was being built. I was part of the board at that time, and also heading the Financial Innovation Council that NPCI had set up. UPI, I’m sure all of you use it. You like UPI? Okay, why do you like UPI? Yeah, small cash is gone. Small cash transactions are largely gone. So it’s a highly efficient system. To give you a bit of a story behind this, in 2016, December, when demonetization was announced, at that time, UPI had just come out, had been in the market for a year and a half, and nobody was using it. At that time, wallets were popular, Paytm was everywhere. So I remember we were looking at the numbers in NPCI, and we realized that in the month of December 2016, exactly after demonetization, one million UPI transactions happened in the country in the month of December, in one month, in 30 days. Today, how much is the number? Can anybody guess? Does anybody know? Yes, correct. It’s gone to about 3 billion, billion with a B, 3 billion transactions a week. And if all goes well in the next 18 months, it will be 1 billion transactions a day. Yeah

There are probably a whole order of magnitude higher number of bombardment happening on the system assuming that UPI will break at any time. And the fact that everybody assumed that UPI will break and cannot scale and has to be designed for scale has led to this preventive model where the system is always far ahead in terms of its abilities than whatever demands we are putting on it from the citizen’s perspective.

So another example. So I would say that these are good examples where quality, I mean Chandrayaan, UPI, we should be all proud. As a country I think we have come to a point where we are able to build these world class systems which worldwide.

I would say that in the last two years, if the two systems have caught the imagination of the world in terms of Indian engineering, it is Chandrayaan and it’s UPI. Worldwide everybody’s trying to figure out how do you deliver such world class systems at such low cost. And one of the principles is frugal engineering of course.

So I won’t have time to get into that so let me move on in the interest of time. I have a few more minutes. So what I’ll do now in the last 10 minutes of my talk is try to stitch all this cyborg issues of scale and the whole software, escalation of software quality problems, complexity problems to talk about the new age quality engineer.

Like I said in my, and I gave a disclaimer in the beginning that I’m not a test professional so forgive me for saying a few things here that may or may not suit our expectations. But fundamentally I think quality engineering is going through a massive change as a field and it has to because of the stuff we mentioned. So let’s talk about some of these things.

Let me start by talking about who’s going to be a great quality engineer of the future. Now historically my experience, you can contest that view, is that quality engineering has always been seen as a very analytical profession. Right? It’s a left brain profession.

If you look at this model I have in front of you, this is called the Ned Herman’s Whole Brain Model where he talks about the fact that you need four parts of your brain to activate well for you to do anything really well in any profession. One is the top left, the so-called analytical brain where you are logical, analytical, data-driven, logic-driven and very, very fact and quantitative, fact-based and quantitatively oriented. Right? So you’re looking at everything through the lens of numbers.

In our profession, in software, we always respect it for looking at spreadsheets. Correct? Or at least looking at your bug databases. So you’re always sort of saying that, okay, talk through data, talk through numbers which we always talk about that.

So that top left quadrant is very important and I think it’s an area where most quality engineers and test engineers are already very good at it. Then the bottom left which is organizing yourself. Right? Are you good at planning? Are you good at organizing, detailing, putting things in proper timelines, timesheets, project management? Can you get things done on time? Right? Can you follow a process? I think that’s also left brain and that also, I think we are good at doing.

What we are not good at doing, in my view, in our profession, is the right side where Ned Herman talked about the bottom right which is the entire relational and the interpersonal aspect of things. Right? So having empathy for the customer. I mean, I see that as a challenge today.

Right? For software professionals, I’m sure certainly for software engineers. For software developers and also software test professionals. Empathy for the customer, I think, is not great.

And understanding why people do, why people use our systems is not very good. We don’t spend too much time worrying about empathy for the customer, empathy for the user. And I think that ability to really say that why are people using my system.

In fact, I always have this mantra that I wake up in the morning and ask this question in talent sprint context every day that why should people buy our products? Why should they use it? Just because we are offering products doesn’t mean that people should buy it. What is it that we are offering that people should buy? And why should they do it? So the question that we ask always is that why should somebody pay for my thing? Right? And how much will they pay for it? And why will they pay for it? So therefore, that question we have to ask a lot more and that requires us to be far more empathetic to our users. So that’s the bottom right.

And the top right is the experimental intuitive part. Are we taking risks? Are we coming up with new theories, new challenges? Are we able to visualize things in different ways? So the idea that our brain has to be able to come up with new scenarios and looking at new ways of looking at things, challenging ourselves, challenging status quo, challenging our bosses, challenging our peers, I think that is another thing we are not very good at. So long story short, in my view, the quality engineer of the future has to have a robust approach to these four dimensions.

It’s being analytical and being organized and cross-oriented is necessary but not sufficient. Especially when the complexity of the world we describe in the case of UPI Chandrayaan, all those examples. I think we are seeing the emergence of professionals who are very, very right-brained.

In fact, so right-brained that I want to take you to my next example where Daniel Pink wrote this book 20 years ago called The Whole New Mind in which he actually predicted that the way AI is going, 20 years ago he said the way AI is going, the left-brained people will actually struggle as time goes on because everything you can do analytically is going to be done by algorithms. And anything that you can do through intuition can be done by humans. So if you are a human professional, your best bet is not to lament the automation of our professions but to improve our intuitions and improve our ability to make right-brained decisions which goes back to the previous diagram.

Relational and experimental abilities. Can we build better relationships? Can we actually experiment and take more risks? If we can do that, then according to this theory of Daniel Pink, we are future-proof as professionals. Otherwise we are not.

If you’re going to say that I’m a great spreadsheets person and I’m a great Microsoft project person, good. But at some point the machines will come for us and they’ll overtake us. All right.

This is the other part I want to mention. If you really look at the history of and this is the only slide on testing that I’ve tried to put together because it’s not a field that I know very well but this is my observation of testing as a field. 20 years ago manual testing was king.

The idea was that humans would design good test cases, test scripts and then humans would test them to death using armies of people and there was a profession called manual testing which made people successful and financially rich. That slowly went away and then in the last 10 years we saw the emergence of test automation. People started talking about automated testing.