Testing and Monitoring Software Automation – Mav Turner, Tricentis

This is texturing TV. Hi everyone, welcome to another techstroke TV interview. I am really happy to be joined all the way from Vienna Austria today a MAV Turner of tricentes.

I should mention Matt's not actually based in Austria permanently, but he's at the chase at this office is there hey MAV, welcome to techstroke TV. Thanks for having me Alan. Looking forward to the conversation.

And as you said I am in Vienna, but I'm actually based in Austin, Texas. And I there I lead a product management for tricenses and you know try to figure out what it is that we need to do in the future what we're gonna build by partner. You know, what's the best way to make sure that that our customers have the full set of solutions to go solve the problems right and make sure we're listening to the customers understanding what they're trying to do and then build our strategy around that and for those of you who may not know, you know, try synthesis is a leader in the test automation market and that's what most people know is for but frankly, we've grown a lot in the last several years not only with test automation but test management performance engineering so really trying to cover that whole lifecycle everything that's important and ensuring and continuous quality story for people that are building deploying managing applications.

Absolutely, and and I just feel obligated to mention that also tricentes is our response for devops Unbound our bi-weekly actually every other week by weekly. Yeah bi-weekly. every other any two different things, I mean twice a week and every other week, so one of those things yeah, well by weekly means twice a week, I forgot what the right whatever it's only another week devops Unbound check it out and we spend about 45 minutes talking about all kinds of relevant topics and devops and Lucky to be working with tricentious going on two years now with that and it's a one of my favorite shows.

Anyway, though maver we're not we're not here to talk about devops and bad. We're actually here to talk about a recent survey that the folks which I said just did and well, why don't you tell them about it? It's kind of your survey.

And yes, so we partnered with tech strong to to run this survey and I mentioned earlier one of the things is very important for us to honestly understand what are users what our customers are doing where they're struggling how their needs are changing and and what they're seeing and we're able to survey about 2600 devops practitioners and this this bandwide role of a wide variety of rules and industries, but we're asking about their AI augmented devops processes, right? So we wanted to understand how they were using AI where they're using AI to help with their processes and to see whether or not that was something Are already impacting or they were just thinking about it and you know to come straight to it Alan. I was frankly really surprised that the large number and overwhelming majority said they were already seeing value from AI power devops processes and we're already expecting even further benefits pretty near here on path.

Now normally even you know, this technology Trends kind of takes a while for them to really have value in an impact, but we're seeing this I think happen much faster than we typically do with new technologies. We're seeing the value of that. That's what that's what the responses to the surveys indicate and it was really interesting to see that that's they're already seeing some of that that data.

you know, maybe I agree with you and I mean here there were two things that sort of surprised me with that one is look. There's and like you right we both talk to a lot of people. And we have a lot of friends.

We've been in this business a long time. There are a lot of people who give you that. Oh that AI is Book crap, right a ice not real machine learning maybe but not Ai, and yeah, it'll be something in the future but it's not anything right now.

It's not real. It's not real. It's not real.

Well, you know what? Maybe it's real number what it's real. Number two, like you said, you know, usually in in technology, you know new technologies tend to run the hype cycle and go into the trial of disillusionment and Gartner lingo before you really get into that Promised Land of hey, it works and it ends value right the height generally, that's our productivity to actually But I think what we're seeing I'm not and I'm not denying that there's not a lot of hype to double negative heart.

There is a lot of hype around AI but there is also real Productivity gains There's real world uses that are working that I think. That I think a reflected in the survey results people don't lie when they I hope not. Anyway, when do you yeah, there's no reason for them to so and that's that's encouraging now.

two main areas that You know, I think in the survey and the research kind of focused in on where AI is. You know starting to have that impact one is testing. Yes, certainly right where we're seeing, you know.

With human automated testing that's being done in tricent. This is one of the leaders there right with the amount of automated testing that's been done that's being done. it kind of is a no-brainer that's actually should be doing, you know, AI kind of stuff and then secondly was in monitoring, I believe that's right, and and a lot of those things that those two domains share in common and the reason why we think that People are already seeing value out of this today.

I mean we said I think 70% of respondents said that the potential of AI augmented testing is it as extremely or very valuable is because if you look at the concepts of AI and machine learning, it's really based on having large sets of data training on right and then having that model be created and then deploying that model in a way that allows you to get benefit when you have very small data sets or data sets that are very unique or Niche. It's kind of hard to get that pattern recognition. So it's almost the ideal technology application of AI and mls in this testing in the monitoring because of the amount of data that gets generated.

So it's not even just a number of tests. It's the amount of data we get back from that test and what we do with it how we parse the responses and and how we go forward with it. And and I think one of the other interesting Trends with that ability to parse that data to bring it back is also what are the tasks that we can automate and repeat all this this video and training content that we can Something to these machine learning models to try to understand how a user would behave right?

We can watch these things and therefore then we can play them back and automate them versus treating a manual test creation and clicking through a UI we can see how users are active you actively using this at scale particularly for for web apps and then create new models around that and then run those models through the application and and pretend to be a user right and so there's a lot of really powerful applications here because of the volume of data that gets created which is why you know AI tends to be such a powerful tool but at the end of the day, it's a tool it's a part of the thing right? We a lot of people want to leave with the AI story you're talking about this earlier, right? Yeah, but really what we usually Talk customer about what business problem are you trying to solve let's focus on that and then we'll find the right tool and right process to help make things easier for you frankly.

a great a great hundred percent so You know, I always try I mean obviously I don't want to say we were surprised at the acceptance and use of AI. But I mean clearly that was a trend in there what else in the survey map kind of caught your eye as well, you know, maybe countering through it over you were surprised. At the real things on when we look at the the the unicorns from a maturity perspective right these organizations that really figured this out and are really executing on it.

Well, we look at them as a as a cohort, you know, when we see that they're the ones that actually have massive benefit compared to others. So it becomes this Advance tool and one of the things that we look at the data we say are you building your own data warehouse or you're building your own tools? Are you partnering?

Are you pulling with a vendor things off the shelf? And that's where I think it becomes really interesting because a lot of these more mature unicorns they invest more in building a data science team, they invest more and and building out their machine learning capabilities or data centers to train their models all the costs associated with that to try to eat out in Advantage, but we rapidly see the ability to monetize those that have vendor level so we can just make that included in the product so that so that everybody can kind of benefit from that so, you know all have to build your own data science team. All have to train your own models and and I think that we'll always have a place for needing how data science teams particularly at a vendor level but the ability of bringing that power down not only to those very mature highly capable organizations, but to everybody is frankly what we see is a big potential going forward continuing to bring this technology down to everybody even organizations that may not be as mature or maybe earlier on in their their process of just implementing a devops process or trying to figure out what do we do next?

Right? How do I go from where I'm at today to a little bit better and a little bit better right every day just gonna get a little bit better. And when do I bring in new technologies and and not say?

Okay, we got to go higher this new staff to go build this capability. Well, we'll start out by learning about the technology partnering with the vendor that makes it easy and then maybe you decide you need to build out an advanced team and there's a business advantage to really honing that out or or maybe decide that you know what we eat out enough value from this approach. Let's look at some of the other options and some of the other challenges in our devops.

Also says that we should be optimizing if we can partner with the right vendors there. Absolutely, and I'll tell you something it's not. so it is a question of devops maturity as you said right people have more experience in this but there's also a data issue right where really for AI and some of these, you know similar Technologies to really sort of You know be really worthwhile and really hit there, you know productivity.

A levels, you need a certain amount of data and look today. We all generate tons of data. But you also need the ability to analyze that data that data analysis which yields the the insights that AI, you know kind of can use to to help and I think that's where as you mentioned as vendors can learn to boil that down and create solutions that you don't need a big data Studio team.

Or data, like, you know data scientists, you know fishing the data Lake to get that information. I think that's a barrier to smaller, you know, not unicorn level. Organizations who you know, I mean my experience map is no one raises the hand that says I want to be a laggard right?

I want it slow person. I want to be the last of my competitors to adopt this everybody wants to right use use the greatest greatest tools that yield the greatest. You know performance but they just don't have the resources.

Right to yeah. I'm really glad you mentioned that because I think that that's something that really is key here where again you want to be able to make that technology accessible and quickly accessible. You say.

All right. If you go back to kind of a couple of decades around machine learning and Ai and some of these Concepts, you know, one of the common topics we talked about was knowledge Discovery from data right kdd conferences and you say that's really what you're trying to accomplish in a lot of this right. So there's there's different ways that the eye where you talk about.

Yeah, we want to be able to, you know, create automation from monitoring a user behavior and then go create a little robot that goes and repeats everywhere. But the other side of that is with all this data. I think it's some knowledge from this data versus I'm a tester.

I know run all my tests and now I get a billion roads of data back what what I need to extract from this it's interesting and valuable. What should I do based on this response in this day? I think that that this is super important to understand which is You can leverage AI to do that anomaly detection.

You can leverage that AI to do the anomaly detection and recommendations on what to do next. And so a lot of these Technologies, like I said earlier are really Prime because there's so much data to to sift through and that's where a lot of them really shine. And so to me that's whether you're mature organization or not.

You need some basic tooling to get through this mountain of data and say what should I do with this? And that's where a lot of these Technologies can really Empower frankly a lot of these users and help mature somebody who's kind of lower on that curve that bumps them up a notch, but even that people that are more mature now, they can focus on something else and that's another theme that I really like to talk about right? We're not we're not trying to replace people necessarily.

We're trying to augment them right we're gonna take this off your plate so you can go work on something else that we can't take off your plate for constantly learning and growing and trying new things and the better we can augment a task that it's kind of defined the more very specific value that that human Can provide and so me the story of AI is always a story of augmentation more more than replacement because even if we take a lot of the repetitive tasks off your plate right now, I can focus on other things and that in that tester or or that developer you look at some of the things like co-pilot right for GitHub. Like it's not to replace a developer. It's to augment them and help them go faster and and focus on other things and I think that's where you see a lot of really interesting capabilities here where people get kind of scared of the oais are gonna come take my job.

No, it's gonna allow the human to go into other tasks and to use this as another cool just like just like calculators and other in computers Excel workbooks have through time, right? Absolutely, absolutely. You know, unfortunately we keep these interviews of 15 minutes or so.

I'd like to talk to you more about it. But I want to make sure we put out here for folks out here who want to maybe see the full report and Analysis of the survey. Where can we send them?

Is it just off the front page of Jason death? Yeah, we can definitely get some links for you. All there will be featured on trisons.

I know these videos live for a while though. So somebody watches this later to you know, that that we will try to get it in the notes for people watching this live. Well not live we recorded it.

But for people watching this, you know late August early September 2022. If you go to the front site the front page of the chase sent to site we should have them up there and it's not looking the notes for URL. Um, hey MAV, I want to thank you.

I know it's late afternoon that I want to thank you for coming on Tech strung TV and joining us. Look forward to having you back on here because I know you not to change subjects, but we're gonna change subjects. I know you guys have several announcements between now and the end of the year.

Hopefully absolutely lots of fun stuff cooking. Yeah, I'm looking forward to hearing all about it. Great.

Thanks. I'm really appreciate the time. It is great.

Just have a great discussion. It's great to have you on math Turner. He runs products over at tricentis coming at us from Vienna.

Today. We're going to take a break here on techstar on TV. We'll be right back.