The Role of AI in QA with Kevin Pyles
Speakers: Kevin Pyles, Director of QA at Domo
Here is the Transcript
You are listening to QA talks, a podcast for quality assurance executives implementing digital transformation in their organizations. In this show, we focus on the unique pitfalls inherent in quality assurance and quality engineering and how these executives are navigating them to position their organization for the future. Let’s get into the show.
Logan: Welcome back to QA Talks. I’m Logan Lyles with Sweet Fish Media. Your host for today’s episode. I’m joined today by Kevin Pyles. He is the director of QA at Domo, welcome to the show today.
Kevin: Thanks for having us.
Cigniti Speaker: Thank you. Thanks for having me.
Logan: Awesome. Well, Kevin, I want to introduce you first. So, Kevin is a proven leader across multi-disciplinary teams, overcoming challenges of downsizing, restructuring, and growth, all of which provide different challenges, providing vision and opportunities to improve teams and facilitate career advancement for individuals. He has an excellent track record of overcoming and breaking down departmental barriers, leading to communication, strategy, and partnerships across different functional teams. Kevin has established vision and leadership to implement new technology stacks to initiate and foster better quality and efficiencies in teams and departmental output.
Kevin, I would love for you to kick us off with a little bit of background on yourself and what you and the team at Domo have been up to these days.
Kevin: Well, thanks for having me again. And yeah, my career started off in support and moved its way over to quality assurance. And like so many others, when you get into quality assurance, maybe land there and you think, what am I doing? Is this where I really want to be? But for me, it’s just super exciting to break stuff. And I love the fact that I can break stuff and somebody else gets to fix it. So, it’s like a dream come true.
Logan: I love that.
Kevin: And then, when it comes to Domo and where we’re at, we’re just working really hard at what the future of testing is going to look like. It seems like for many years, testing, it was even stagnant. There’s been very little change. But now with AI and just a lot of players in this space, we’re seeing a lot of developments that QA engineers now have to be really on top of their game and looking forward to what’s coming and learning new tools, techniques, frameworks, whatever else comes out and applying that to their testing trade. So, yeah, we’re crushing it over here at Domo’s as much as we can and really excited for the future.
Logan: Yeah, absolutely. I love the way that you describe, one landing in QA. And then also, what that means you get to break stuff that other people have to fix. That’s fantastic. And you touched on a hot topic that I think is going to be a good portion of our conversation today, Kevin, and that’s AI. We’ll circle back on that in just a moment.
Logan: All right. Kevin, I want to kick it over to you with this first question about some of your experience. You’ve created Python, AI, and ML training programs for testers. Can you share some more details about this and how it’s helping testers today?
Kevin: Yeah. So, again, back to the vision that I introduced. We’re looking forward to where testing will be a few years from now. And we started this again a few years back and we saw that AI was up and coming. Maybe it’s something that we should get interested in. And my team was just super excited about it. We heard a great presentation and we said, let’s do this. And we jumped right into artificial intelligence and we were just drowning. It was a catastrophe, really. We had no idea what we were doing. And so, we decided to back up a little bit. OK, what should we learn before we learn artificial intelligence? We said, well, maybe we should learn some data analytics. And we realized we don’t even know how to write code. So, we should probably start with that. And that’s where we decided on Python. Why Python? Because Python is used for automation and testing as well as artificial intelligence. And we started out with really two different experience levels.
We had people who were really good programmers and people who hadn’t written any code. And that created a great divide. And I decided, you know what, we can’t leave these people behind that are new to testing and new to coding. And we created an introductory program for Python and used a book called ‘Beginning Python’ and created some exercises for everybody to get up to speed. And then each of the engineers were actually required to demo their work. And at first, we thought this would also be a disaster, experienced engineers, why would they want to listen to these beginning exercises? But we found that the new coders actually had different ways of looking at things. When you get stuck looking at code for 20 years, you maybe look at it one way and when you have somebody new who’s never written any code before, they look at things differently.
So, both the experienced and the new programmers for learning things from each other as we develop this Python framework, Python learning program. And that then evolved into how do we learn data science, how do we learn machine learning. And so, I’ve actually put up this program on GitHub where people can go and follow. I’ll say, step by step, but there’s a little jumping around that needs to be done to learn all the way from Python to automation, data science, and then machine learning for testing.
Logan: I love the way that you guys take took your experience and where you were kind of heading and backing up a few steps to get your feet under you, and I think the experience that you shared there and what that process was like is can be really valuable to a lot of folks, Kevin, as we look at then the next steps.
Obviously when we say AI, people realize, OK, this has been a buzz word, it seems like for a while now. There’s a lot of talk, a lot of confusion around AI and machine learning as you think about the future. Where do you see AI actually helping businesses where the rubber meets the road in the years to come, Kevin?
Kevin: So, the vision, the hope for everybody, I think, is someday, AI will be able to do the testing for us. We’re not there yet. I’m not promoting that. That’s what’s happening right now. But what is definitely here is AI–based tools and AI that helps us with our jobs. So, we shouldn’t look at it as AI replacing testers yet, we shouldn’t look at it as AI replacing really most of our processes yet.
What AI does right now is it helps us be better testers, meaning it takes out some of that mundane work that we wouldn’t like to do anyway. Or maybe as we’ll hear a little bit later, AI can help us do things like prediction or analytics better than we’ve done in the past, which just allows us to do our jobs better. And I think that everyone should, at this point in time be on a roadmap, on a path to at least understanding how AI is helping. And if you’re not, you are missing out. It’s kind of like only using a hammer when you really need a screwdriver. There are just tools now that use AI that can help us do our jobs better. That’s where I see it helping businesses right now. And of course, as it progresses, will gradually integrate it more fully into our processes and into our businesses in the future.
Logan: I love that perspective and really great context there, Kevin.
Cigniti Speaker: Absolutely. What I see, of course, doing testing takes time and a lot of investment. And by the time that the test code is completed and you around your test, the requirements are changing, and applications are progressing with changes to business functionality and UI. And so, your test automation needs to be adapted. So, this is what I call test automation trap that actually you’re not getting enough time, the test teams are not getting enough time to be able to do the failure triage from the previous test run before building your next test automation code. And I guess that is where I mean, yeah, can be really used to solve this dilemma and to accelerate the manual testing. So, with some of our clients, we are able to apply AI in the context of prioritizing test cases and also maintaining the test automation code in an automated manner, as opposed to manually investigating what needs to be changed. And I expect that over a period of time, like Kevin mentioned, we are not there yet. But then over a period of time, we see that it can play a great role in the context of analyzing the test results and also deciding on what needs to be tested and things like that, which can happen freely without human intervention.
Logan: I appreciate that. I think you and Kevin are doing a fantastic job of breaking down into specifics of this huge topic that is talked about a lot and bringing it back down to the day–to–day. Kevin, I want to kick it back over to you. We talked about some of your accomplishments in the training programs you’ve created. Something else you’ve worked on recently is developing a tester dashboard. Congratulations on this. I’ve heard good things. Could you tell us a little bit more about the challenges your tool would address here?
Kevin: Yeah. So full credit goes to Domo on this one. For years I wanted a tester dashboard and that sounds like a simple thing, but the reality is we use so many different tools as testers and gathering all of that data into one place is just a really big challenge. That’s a big enough challenge. But once you’ve gathered the data, you want to create these cool charts. Okay, fine, you can create charts on various tools, but how do you share this data with the rest of your company? And we came up with ideas like where we could put a TV up and then we can have a computer connected to it. So, we could pull the data and then it’s like, well, yeah, but who’s going to update the data? And people only see it when they walk by it. How often do your executives really walk by your TV that’s in some dungeon somewhere? So, the challenge was how do we get a dashboard that people are actually going to see? And again, full credit to Domo. When I came to Domo, I was able to build a dashboard in a very, very short amount of time. And the advantage to the Domo dashboard is I can pull in my data from anywhere and then I can visualize that data using hundreds of different chart types. But then really important is being able to share that data with everyone. So, with Domo, I have the Domo app on my phone. All of my executives have the same app on their phone, and I can just share my dashboard with everyone throughout the company and they can see the status updates in real–time using JIRA content, test drill, enterprise tester, GitHub, whatever and wherever that data is located. We can bring all of that data into one place and then share it across email, the Domo app, or numerous other ways. So that’s the power in this dashboard. And of course, from my perspective, the power is in the – I have three or four cards, Wukong cards or charts that we put on this dashboard. They give a quick status update. And I believe they answer just about any question that executives would ask – So, what’s the status of the project? Are we ready to ship? How many bugs are coming in? And then how do we feel about the release? And all of those questions are answered using this tester dashboard.
Logan: Absolutely. Anytime you can get data from different places all in one spot, that’s going to help out any team for sure, Kevin. Speaking of dashboard, Cigniti has also recently developed a predictive dashboard and it’s been deployed at several enterprise accounts. Can you tell us what this dashboard is and some of the unique qualities of it?
Cigniti Speaker: Absolutely. So, the dashboard that you are talking about, we call it as Quality Engineering Dashboard. And the internal name that we gave to that is Verita, which means basically a single source of truth. And if you really look at it, as more and more organizations are moving into agile and DevOps and there is a great need to get real–time insights and analytics. And so that the teams are able to act decisively in terms of the changes that need to be done to the projects. And of course, something like this is also needed to be able to make release readiness decisions. And the dashboard that we have built, it comes with the ability to analyze the data and also provides the data from a descriptive, diagnostic, predictive, and also prescriptive point of view. So not only it can tell you what happened, but the dashboard, we sort of included the AI-ML features into the quality engineering dashboard. It also has the ability to predict what could happen based on their previous projects of similar size and scope. And also, the portal that we’ve built, basically the quality engineering portal, it has the ability to be configured to different roles so that every new individual and being, they have got the necessary things that are important for them. And in essence, the dashboard that we’ve built, it is ultimately built to drive better business outcomes and improve predictability and also accelerate the transformation that is happening within the organizations.
Logan: Yeah, I really appreciate that breakdown. Kevin, we’ve talked about some hot topics bringing data together. AI talent both in QA and beyond is a hot topic, I think, for every leader, regardless of what department or function they head up. Could you take a second to give us your take on retaining top tech talent as well as finding that talent retaining it? I think for leaders listening to this, that would be great to hear a little bit of your perspective on that today as well.
Kevin: When it comes to recruiting top talent. It’s really important to have a vision that people get buy–in. And we can put all the beanbag chairs and free meals and whatever other perks that are hot right now in front of people. But the reality is engineers are jumping company to company. And the reason they’re doing that is because they’re not bought into the vision of that company. And there are two aspects of that. First off is we have to have a vision, but then we have to share that vision and get people to buy into that vision. When they bought into the vision, they’re not there just for a pay check. They’re there for a bigger cause. I think that’s something that’s really helped with my team, is the team is interested in this greater vision of how do we apply enough testing, how do we learn about AI and how do we build these skill sets and then how do we apply this not just locally for our team, but I have people that are now speaking that would never have thought of speaking to large audiences. And they’re able to do that now because they’ve caught that vision and they have this strong desire to share what they’ve been learning with others. Now you have people that are advocates for what you’re doing, and that’s so much more than just an employee, right. When we have advocates for the cause that we’re about. And so that that’s how you recruit people because people see that excitement. They see that vision. They see that mission. And then they want to stay because they want to be part of that greater cause. And I think if we could do that yet, people still need to get paid. They still need to buy their homes and their cars or whatever else it is that they desire. But they’re more out to stay longer. Especially when you’re generally offering similar compensation. That vision is what’s really going to keep people.
Logan: Yeah, I completely agree with you there, Kevin, and I love the way that you broke it down into two steps. You have to have a vision, right? I think there are many organizations where that vision statement is collecting dust on a wall somewhere or in a plaque. And then you have to actively share it. When it comes to differentiating yourself from other big players in your market in recruiting top tech talent, do you think that sharing that vision more publicly is one of the key ways to be able to do that?
Or do you have any other strategies or thoughts around that in that differentiation pieces as talent is becoming more competitive to be able to bring those people into the organization.
Kevin: I think that’s a great question, really. The more you can advocate that vision, the more people are just drawn to what you’re doing, right. And you can see when companies have lost sight of that vision. Look at some of the big tech companies that are out there. They grew really fast and everybody wanted to work for that company. Why? It wasn’t because of the money, although people are definitely drawn to that. It’s because of that bigger vision. And then once that vision has kind of waned and people say they really care about that anymore, are they really focused on that? That’s when people start to drop off or people don’t care to work for that company anymore. So, in my case, some things that we’ve been doing is just getting out into the local community. That’s where we’re most likely going to hire the talent. And I’ve had people come up to me after presenting at these meetups and say, I want to work for you. And that’s like, what do you mean you want to work for me? I want to work for somebody who has that vision, who cares that much about where they are and where they’re going, that it’s not. They even say it’s not about the company, although it helps that they work for a great company with great vision and a great product. But they also say, I’d like to work for you because you can see that, and you can help me in my career to accomplish this vision. And, in my case, it’s about pushing AI and testing forward and doing great things in this industry. People are drawn to that
Logan: Yeah, absolutely. I couldn’t agree more, Kevin. Well, I really appreciate your thoughts there on talent retention and developing that vision that people can get behind and draw them in with that as well. Two more topics I want to cover with you gentlemen while I’ve got you before we round out today and that is DevOps as well as the role of IP. On DevOps real quick, Kevin, there’s a lot of discussion around DevOps and the rapid pace of adoption really across industries. Can you give us your perspective on this as we kind of wind things down today?
Kevin: This may not be a popular opinion. DevOps is very similar to all of the other, I guess, transformations that have been either claimed and, in some cases, applied. We can look back and we had Waterfall, which was actually a name given to product development after the fact. Then we had agile. Now we have DevOps. There’s been a rebranding called DevSecOps. And I was actually at a meetup recently that called it QASecDevOps just to make sure we got everybody involved in this transformation. And so what? The way I look at DevOps along with although their transformations are what you really need to do is look at your business. What does your business need and what best practices are good practices out there that can be applied to your company? And why are we always looking externally for solutions when we probably have a lot of really smart people internally that could help us develop our own techniques and business processes, dev processes, and techniques. We should probably do a better job there and we can improve what we have. Because a lot of times, the Facebooks, the Googles, the Microsofts with, thousands and thousands of developers can do is different from the rest of the world. And so, we need to look at what makes sense for our companies and apply whatever new terminology we want to apply. But more important, apply practices that will help us develop better products that suit the needs of our customers and our businesses.
Logan: I love it, Kevin. I love a passion and opinion on anything, and you’ve definitely given us that on a few areas. I want to kick it over to you as we got a little bit of time left here to talk about the role of IP in quality assurance. Can you tell us a little bit about Cigniti’s investments in this area as of late as we wind down today?
Cigniti Speaker: Absolutely. Since the beginning of the company, we just didn’t want to be another services company. We want to be a services company with strong differentiation. And that was the reason why we dialogued Cigniti IP, which we call as a BlueSwan. And the BlueSwan is a next–generation proprietary testing platform that has got a set of tools, process assets, and utilities. And the way to think about the IP, that we have developed is that say that there are two testing units with equal skills, and one is equipped with BlueSwan and the other one is not. The one that is equipped with BlueSwan is going to be a lot more productive in what they do and because they have the right toolset to do it. And the BlueSwan itself, this IP, it has got a great impact in terms of reducing the testing costs and also greatly improving the efficiency and effectiveness of the overall testing as well. And it is domain agnostic, which means that it could be used across all different verticals and also with different company sizes as well.
Logan: I love it, Thank you again for some very specifics to something that I think a lot of our listeners are keen to know more about. Kevin, as we wind down today, I would love for you to share with listeners if folks listening to this have follow up questions or would like to stay connected with you or learn more about what you and the team at Domo are up to these days, what’s the best way for them to reach out or learn more?
Kevin: I’m on LinkedIn and that’s probably the best way to contact me. You can just send a message there. But also, on Twitter and love to hear what people are doing on either LinkedIn or Twitter. Those are the best options. But if you contact me there and we need further conversations, I would be happy to give out my email.
Logan: It sounds good. That’s the ways for me as well. Kevin, I appreciate it. All right. How about you? What’s the best way for someone to reach out if they’re interested in anything that you’ve talked about today or just delving more into some of the Cigniti’s technologies that you’ve shared with us today?
Cigniti Speaker: Absolutely. I mean LinkedIn is the best way for me as well and I can also be reached at firstname.lastname@example.org.
Logan: All right. Thank you so much for your contributions today. Kevin, thank you so much for joining us as a guest. I really enjoyed the conversation today, gentlemen.
Cigniti Speaker: Thank you for having us.
Kevin: I really appreciate the invite and the conversation. Thank you.
Quality assurance is vital to the success of an organization’s digital transformation. Lack of control can quickly derail a company’s technological presence, costing thousands. At Cigniti, our resolution is to build a better world with better quality software. Renowned for the global quality thought leadership in the industry, we draw expertise from over a decade of test engineering experience across verticals. To learn how we do it, visit cigniti.com.
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