WEBVTT
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You're getting pressure to deliver projects on time.
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You're getting pressure to take, the millions of dollars in workforce that you have and ensure they're working as efficiently as possible and then report that back to the board.
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Right.
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You're getting pressure to make sure that the developer's experience is fantastic, because these folks can work anywhere they want in high demand, right?
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So if you're like the VP I think software engineering intelligence helps you to push one of your initiatives that would have been very difficult to do without data.
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Gardener just released their market guide, showing that software engineering intelligence platforms, help engineering leaders significantly improve both team productivity and value delivery.
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Through Gardner's in-depth analysis on the critical features of sci platforms and how they can be used to drive engineering excellence.
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Linear B was named as a representative vendor.
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And therefore we're giving away a complimentary copy of Gartner's sci market guide.
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Head to the link in the show notes to download your complimentary copy and learn how you can unlock the transformative potential of software engineering intelligence for your team.
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Hey, what's up everyone?
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We're back with another Labs episode of Dev Interrupted.
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I'm your host and linear BCOO and Co-founder Dan Lyons.
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Today I'll be joined by my co-host, Connor B.
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Great to see you as always, Connor.
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Likewise, Dan.
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Always great to be here and to hear about how badly your fantasy baseball team is doing.
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I know we were just talking before, we're at five and six.
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We're battling for a playoff spot.
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You and I do fantasy football together.
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I think fantasy football is my number one.
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Fantasy baseball is more like, tie me over until football season starts, but definitely.
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Learning a lot about baseball.
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What I love is that you, not only in your career, but in your personal life, just, you love data.
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You're like, Hey, I really want to spend my free time figuring out data around a sport.
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Uh, you know, in my work life, you know, let's do data around engineering teams.
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How can I optimize these teams?
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They perform better.
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You know, it's almost like you have an obsession here or something.
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That's actually funny.
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I think, honestly, probably a lot of our audience has seen Moneyball.
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Moneyball is like what turned me on to fantasy baseball.
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Mm-Hmm.
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Yeah.
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Checking the
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data, looking at the box score.
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Where's the optimization we can find people aren't necessarily seeing so far.
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Yeah.
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A little nerdy, but yeah, I like it.
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Um,
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well, and I think that drives into kind of the things that we talk about with dev, developer productivity and developer experience, like.
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What are the, the workflow challenges that we can find, or what are the things that people are underrating?
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You know, slugging percentage, maybe for an example for baseball, that people aren't rating as highly as they need to.
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Um, our strikeouts a big issue, or you know, our, our long PR is a big issue, like these kind of optimizations.
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Yeah, I think the cool thing is like, Let's say that you're observing baseball or like you're observing how your engineering team works and you might have like an intuition or a theory.
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This happens all the time, both in business and sports.
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Like I'm watching a game.
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I, and, and sometimes I'm proven right and sometimes I'm proving wrong.
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Like I think a player is getting better.
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Actually, like the underlying data says that's not the case or vice versa.
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Like.
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Something on my, uh, in business is like, Oh, I think we might have a problem in this area of the workflow.
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Actually, the data says we're okay.
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It's somewhere else.
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So I think that's also cool to see like that.
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Hey, this is what I think's happening.
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What's the data show me that type of thing.
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So yeah, maybe that's where the fantasy, uh, is.
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Uh, Sports Addiction comes from.
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Well, and I know that's the topic for what you want to chat about today, right?
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Which is, you know, how are we understanding the problems and then solving them that are happening in software engineering?
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Yeah, I think we have a cool topic today And I'll start out I think by just saying earlier this year Gartner released their debut SEI platform market guide.
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I'm sure most people on the pod know what You What's up with Gartner?
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And I think it's really important because finally, kind of like all this stuff that we've been talking about with data, maybe since the beginning of the pod has come to fruition that it actually has like a formal name and is recognized and it's this thing it's called SEI and There's a lot of love for it.
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Gartner estimates that adoption of SEI platforms, they're poised to increase about 50 percent of engineering teams will have one, uh, one of these SEI solutions by 2027 compared to only a 5 percent increase this year.
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So it's like hot topic picking up.
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That's where we're going to dive in today.
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What is up with this SEI stuff?
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Yeah, so maybe we can start with the basics, Dan, since I know we talk about a lot of these kind of areas, these initiatives, uh, on the podcast, but, uh, what is software engineering intelligence and what is a software engineering intelligence or SEI platform?
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Yeah, well, we probably should have said that first, right?
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SEI, Software Engineering Intelligence, that's what it stands for, but actually it's kind of evolved since I've been this and you've been doing this six, seven, eight years now.
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I would say the first incarnation of it, I would describe it something like this.
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Okay.
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SEI, it's this data driven visibility and insights into how engineering teams operate and improve their efficiency.
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That's maybe how I would talk about it like five years ago.
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And it's not that it's wrong, but I actually think the space, uh, has evolved.
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We've all evolved with it.
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And now I would say.
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Actually, it's still about being data driven and providing visibility and providing insights, but more importantly, it's really how our product delivery organizations transforming in a few different areas, like their on time project delivery, the allocation and alignment of their costs, developer coaching, efficiency, dev experience.
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It's really about transforming these.
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I would call them more like, uh, like business value initiatives with data.
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And that's more so where, where I think SEI sits today, which, which is a great evolution from just like data and visibility to like really moving the needle with the business.
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And this lines up with this dual mandate conversation we've been having here for podcast of like, how do we both improve operational efficiency within engineering orgs and then align that engineering effort with business goals.
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And I know a couple of the key capabilities that we're seeing the, the best software engineering intelligence platforms have are that ability to turn insights into action.
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Via automation or other opportunities, and then that other side of things around how can we understand the investment, the resource allocation that we're making at a team and project level for engineering teams?
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Yeah, I think you're kind of hitting on some of the I don't know, I would say like the core features or core capabilities or maybe the best way to describe it is, let's say that you want to run a software engineering intelligence program at your company.
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Say that you want to do that.
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Okay, you understand the space is heating up.
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You got to get data driven.
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Now you want to start an initiative.
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What do you need to do?
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So maybe let me describe some of the things that make up SEI.
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I think first and foremost, it's around predictable or on time project delivery.
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I'll give like the, the real stories behind that.
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I guess that's the other thing I like about this SCI space now.
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Went from just like this, yeah, measure everything with data to like real life stuff.
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Real life stuff is, I'm a VP of engineering.
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The number one question that I get asked is, when is the project going to be done?
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When's the feature coming out?
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That's what I'm being asked by my CEO.
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That's what I'm being asked by the sales team.
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That's what I'm being asked by the product team.
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So the first core module of SEI is what I call on time project delivery.
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That's probably my favorite one.
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Now there's a bunch of other ones.
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I'll list them all out, but maybe, maybe that's the first one.
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The other ones are around profitable engineering, resource alignment.
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Are we investing into the right things?
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Where's the money going into our projects and people like that's super important.
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Again, it's tied back to the business, not just metrics, but it's like CEO comes to you and says, Hey, I'm giving you a few million dollars, right.
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To staff your development team.
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Where is that money going?
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And what's the outcome for our business?
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Right.
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Yeah.
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So, so you mentioned this phrase, profitable engineering, and I, I've started to hear that now in the industry lately.
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What does that mean to you and why should engineering leaders be thinking about engineering profitability?
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First of all, I think it sounds really cool.
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Profitable engineering.
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It's catchy, right?
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Absolutely.
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Let's like do the non catchy version.
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I think the non catchy version is something like, Take your project costs, so the cost that it takes to develop every project that you're working on, okay?
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And do a translation of that cost back to your business folks.
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Again, could be CEO, could be anyone on the executive team, could be your board.
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And justify that you should be investing in project A.
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And you should be investing, let's say 25 percent of your engineering workforce, which may be millions of dollars if you're a bigger company.
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And then how can you translate to say, this is how I think it will help the business.
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That's how I think about profitable engineering and Conor, what I would say, it's like so important actually for software engineering intelligence.
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Cause again, if you're just looking at metrics of like cycle time, not saying cycle time's bad or something like that.
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But if you're just looking at like a cycle time metric, you're pretty far away from talking about how this affects your business, right?
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And with these things like on time project delivery, profitable engineering, I think it kind of elevates your career and like elevates the engineering team's importance to the business.
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And that's what I like about it.
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So why do you think these software engineering intelligence platforms are coming to the forefront now?
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Yeah, no, that, that's an awesome question.
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I actually thought a lot about it over the last few weeks, cause that's what I do in my spare time.
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And I don't think that there's one reason, but there's a collection of reasons.
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The first thing is, I think there was like this collective consciousness that everyone at a similar time within engineering and also the executive team said, Hey, I really think data can help us.
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Like if we visualize the data of how engineering works, how product engineering works, I think it can help us.
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And the reason I think it all happened at the same time or similar time is because you saw that in all different areas.
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Like sales has data, marketing has data.
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They've had it for a long time.
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But even like, uh, HR has data now, People Analytics, Engagement Survey, so that, that's like one thing that happened.
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Then the second thing that happened is, our ability to collect this data is easier than it's ever been.
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If you think about LinearB, we're connected into your Git environment, we're connected into your project environment, we're connected into your chat.
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We can see all of your releases.
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We can see everything with CICB.
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We can see all the tooling that you're using in the cloud.
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Sneak.
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So it's like not only did everyone say, Hey, I think this data would be useful.
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We can actually get the data pretty easily.
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Now you can get LinearB up and running with a few clicks of a button.
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That wasn't possible a long time ago.
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All right.
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So I think that's the second reason.
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Then I think these two other ones are more like what's happening right now.
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Every company is a software company now.
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You have companies that sell, I won't say the company's names, but you have companies that sell burgers, cheeseburgers.
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They're software companies.
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You order your burgers on your mobile app.
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They're getting more mobile app orders than go up to the counter orders.
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They have thousands of developers working.
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You have companies that sell tractors.
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Actually, there's a ton of software going into how, how do you, um, Look at all of this equipment and see what's working and what's not working.
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How do you maintain agriculture using drones and all this like insane technology, soft farming, farming is like software now.
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So it's like, I think the third thing is almost every big company is a software company now, and they have recognized that and they have a large development team.
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Classic example of this is like Domino's, for example, where it's been this exemplar of like, hey, they became a tech company and transformed to take on this new stage.
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Yeah, but to your point, everyone's doing it now.
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Software has eaten the world.
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So I think, I think that's the third reason.
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And then the fourth reason, I think this is the kicker.
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I do think the AI movement is helping a lot.
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we're working with some companies where 25 percent or more, 40%, 50 percent of the code, specifically pull requests, are raised by bots, not raised up by humans.
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By bots.
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Wow.
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And people want to measure not only the impact of things like Copilot, like is this helping, but also how do I improve the developer experience?
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Because we have so much code coming into our system now that's not necessarily human generated, like what are we going to do to improve?
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And I think if you put those four things together, that's why the space is now mandatory, let's say, or exploding.
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Do you think that kind of every level of an engineering team can benefit from the software engineering intelligence solutions?
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Yeah, I do, but I think, I think that they do it in different ways.
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So the short answer is yes, but it's like different depending on where you work, right, in the org structure.
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So if I'm a dev versus, you know, maybe like a director level, manager level person versus like the VPE, what are the different ways that those personas are going to benefit from SEI?
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So if you're like VPE or CTO, I honestly think the best way that SEI helps you, it's to push one of those initiatives that I just talked about.
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Like you're getting pressure to deliver projects on time.
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You're getting pressure to take, the millions of dollars in workforce that you have and ensure they're working as efficiently as possible and then report that back to the board.
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Right.
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You're getting pressure to make sure that the developer's experience is fantastic, because these folks can work anywhere they want in high demand, right?
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So if you're like the VP I think software engineering intelligence helps you to push one of your initiatives that would have been very difficult to do without data.
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Now if I keep going, because I think you asked then about like the manager or the director?
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Yeah, the person who's maybe managing the platform engineering team or managing a team of devs.
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I think it's one of the hardest jobs in engineering.
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We have a bunch of, I think, earlier episodes, like being an engineering team leader, being a group manager.
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I think that's the hardest job, this middle management layer.
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I think it's that for a lot of industries, but definitely in engineering, you're doing this combination of decision making and managing people, but also, uh, delivering on sprint predictability.
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And you have to be technical, but you also have to have the soft skills.
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There's a lot coming at you.
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And for example, what we're doing at LinearB, you can think about it as like a co pilot for engineering managers.
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Deliver these people insights so that they can be, you know, developers are always like complaining, I have a terrible manager.
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It's because 50 percent of the time they probably do have a terrible manager.
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It's really hard.
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But if we can get this data into the hands of each one of these decision makers, a lot of them are new, by the way, I've been, maybe I've been doing this like two years, one year, they can make better decisions, right?
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So I think like, uh, at the management level, it's like day to day decision making with data.
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It's supercharged that.
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Helping coach your devs too.
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Yeah.
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Oh yeah.
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Of course.
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Of course.
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Like I'm, I'm now responsible for coaching, uh, 15 devs or whatever it is, eight to 15 devs, maybe more.
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And maybe I didn't even mention that as one of the initiatives, but definitely having a developer coaching program that is more data driven and fair for developers.
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How do I grow my career?
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Am I contributing to the team in the right way?
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Am I contributing to pull request reviews, or am I only coding?
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That's definitely an initiative that a lot of the VPs are pushing, and it's hard on managers.
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It's interesting because a software engineering intelligence can really help engineering teams navigate change, whether that's on the personal front, where, hey, I'm trying to, you know, improve my career.
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I'm trying to level up.
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I'm trying to move to a new role.
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Uh, let me get quantitative and qualitative data, and then automations.
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And other unblocking opportunities through the platform that can help me to improve as an individual or as a team leader.
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And that it really helps manage, let's say we acquired a new company.
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Uh, how can we now evaluate how they're integrating with, uh, the rest of our engineering or the rest of our product org?
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Um, I mean, there's a ton of examples of this.
00:18:25.127 --> 00:18:36.928
We go on, but like this ability to better navigate change, uh, is so crucial to resiliency for software engineering teams and, uh, not only can it help you scale, but it can also help you navigate a lot of these other challenges.
00:18:37.478 --> 00:18:43.137
Well, just like a real, real life example, a real life, real life example, we're working with a company right now.
00:18:44.028 --> 00:18:49.248
And I think everyone listening, if you're not a developer, you can remember what it was like to be a developer.
00:18:49.317 --> 00:18:53.228
And actually, the career ladder is a little bit vague.
00:18:53.238 --> 00:18:57.278
Like, what's the difference between a junior developer and a mid level developer?
00:18:57.988 --> 00:19:00.597
Or a mid level developer and a senior developer?
00:19:00.597 --> 00:19:03.317
Or a senior developer and a principal developer?
00:19:04.087 --> 00:19:06.968
In a lot of organizations, it's not defined that well.
00:19:07.417 --> 00:19:18.117
And sometimes, in order to get promoted, it's the type of person that can have A highly articulated conversation with their manager and push for their career and all of that.
00:19:18.397 --> 00:19:21.008
Now the company we're working with, they want to make it more fair.
00:19:21.008 --> 00:19:22.728
There's all types of developers, right?
00:19:23.298 --> 00:19:31.137
Not every developer is like an orator or maybe come on a podcast and can like prove their case of why they should be a senior engineer.
00:19:31.137 --> 00:19:39.472
Now they're saying, hey, with data, we just want to show the capabilities that it takes to be junior, mid, Senior and Principal.
00:19:39.623 --> 00:19:45.853
And for them, for example, one of the big things that they're pushing is how do you contribute to helping other teammates?
00:19:45.913 --> 00:19:57.742
And they're doing that by saying each week, how often are you providing feedback on poll requests and giving comments and helping others and not just doing your own work?
00:19:58.663 --> 00:20:00.732
And they can see that with the data.
00:20:00.732 --> 00:20:10.262
So now when they have that one on one conversation, it's not like you only get promoted from that, but you could say, Hey, Conor, yeah, I can really see that you are helping a lot, actually.
00:20:10.262 --> 00:20:16.573
It picked up from like almost zero, you know, reviews that they're helping with or one per week to like two to three.
00:20:16.623 --> 00:20:17.742
Can we talk about that?
00:20:17.782 --> 00:20:18.863
I saw that out of you.
00:20:18.863 --> 00:20:19.712
That's awesome, man.
00:20:20.268 --> 00:20:22.117
So it's really like career progression.
00:20:22.570 --> 00:20:30.724
Really interesting, and it sparks something for me on the platform side of things because you talked a bit earlier about how this space has evolved.
00:20:30.734 --> 00:20:42.835
You know, it used to just be kind of, oh, let's get some basic engineering metrics so we can understand what's happening, to now really providing these coaching opportunities, uh, improving developer experience and improving developer productivity, some of these big initiatives I want to talk more about.
00:20:43.434 --> 00:20:53.384
But there is also other concept that we've talked a lot about on this pod that I think relates to SEI and I want to understand how you see them as differentiated or aligned, which is value stream management.
00:20:53.694 --> 00:21:02.055
You talked a bit earlier about predictability in the software engineering process in this and trying to bring that predictability and profitability to your software development lifecycle.
00:21:02.414 --> 00:21:10.404
How do you see this concept of software engineering intelligence and what platforms that provide SEI can deliver relating to value stream management?
00:21:10.708 --> 00:21:21.093
So when I When we first founded LinearB like six, seven years ago, there was this concept of VSM, Value Stream Management.
00:21:21.393 --> 00:21:38.823
And there was a big promise there of, I'm going to take every piece of value from you're thinking about a piece of value to you have an idea about it, to you put it onto paper and I'm going to track it all the way out to the customer, getting it.
00:21:39.218 --> 00:21:42.798
Then I'm going to track it all the way around to renewals.
00:21:42.807 --> 00:21:47.657
And I'm going to therefore manage my value stream and we're going to get better.
00:21:49.147 --> 00:22:03.057
And unfortunately, what I've seen with a lot of VSM is the actual rollout and implementation of what I just said is extremely difficult and therefore.
00:22:03.712 --> 00:22:09.383
It's an amazing idea in concept, but I haven't seen it actually implemented very well.
00:22:10.042 --> 00:22:23.303
Now with software engineering intelligence, if you think of VSM the way that I just described it from like I have an idea all the way to like a customer getting value and renewal, I think what's great about SEI is it's more down to earth.
00:22:23.472 --> 00:22:24.903
It fits in the middle of that.
00:22:25.313 --> 00:22:27.752
It's four product delivery teams.
00:22:27.752 --> 00:22:30.883
It's kind of like in the middle of that whole VSM end to end.
00:22:31.407 --> 00:22:36.837
And you can roll it out and you can get all the data for it and you can actually improve.
00:22:36.837 --> 00:22:38.167
So I don't know.
00:22:38.178 --> 00:22:44.498
I'm not trying to say one's better than the other, but I think like SEI became, I would say more down to earth and achievable.
00:22:45.178 --> 00:22:46.508
And that's the big difference.
00:22:46.893 --> 00:22:54.242
So maybe SEI platforms provide an opportunity to actually realize the value of ESM and say, okay, how do we implement this?
00:22:54.242 --> 00:22:57.012
How do we actually put this into practice?
00:22:57.313 --> 00:22:57.903
I think so.
00:22:58.239 --> 00:23:03.588
You've mentioned a couple other key initiatives that are common themes or refrains on this show.
00:23:04.259 --> 00:23:11.608
Developer productivity and developer experience as things that SEI platforms can help teams solve or improve.
00:23:11.979 --> 00:23:15.199
How do you see SEI driving developer productivity?
00:23:15.640 --> 00:23:20.779
Well, it's so great because those two things, productivity and experience, go hand in hand.
00:23:20.930 --> 00:23:21.920
That's what I love about it.
00:23:21.920 --> 00:23:24.549
Sometimes I think it's just like the way that you're viewing it.
00:23:25.869 --> 00:23:29.700
Being a developer, at the end of the day, you want to get your code out to prod.
00:23:30.509 --> 00:23:40.210
You want to, a lot, a lot of developers want to see the impact that they make on the business in terms of like customers using the cool stuff that they create.
00:23:41.200 --> 00:23:44.970
And if I just simplify it, it's like, what's preventing me to do that?
00:23:44.970 --> 00:23:50.869
Where's the toil located where it's like more difficult for me to actually get my job done?
00:23:51.480 --> 00:24:02.700
And with SEI, it actually measures the workflow from coding time, to pull request review time, to pick up time, to deployment time, to how long are the tests running?
00:24:02.710 --> 00:24:09.410
So therefore, it's kind of like measuring that experience for the end, for the engineer, what are they going through?
00:24:10.454 --> 00:24:17.164
And when you turn on an SEI platform for an organization, most of the time you will see a bottleneck in that workflow.
00:24:18.065 --> 00:24:33.295
And if you say to yourself, hey, I'm actually going to use some automation, I think that's the other part of the space, let's use automation in order to fix this workflow problem, your developers will be more productive, but they'll also have a better experience.
00:24:34.035 --> 00:24:36.394
So most of the time it goes hand in hand for me.
00:24:36.821 --> 00:24:44.201
It's really interesting you mention automation because I know this is an area where LinearB is taking a different approach to many of the other SEI platforms out there.
00:24:45.402 --> 00:24:50.392
Turning insights into improvement by actually providing automation's built in platform.
00:24:51.132 --> 00:24:59.027
And it's exciting to see That you can now go into the LinearB platform and kind of just add an automation that relates to, Hey, I want to solve this use case.
00:24:59.336 --> 00:25:04.787
Can you explain a bit more about how you view LinearB as going beyond the basics?
00:25:05.297 --> 00:25:13.047
Yeah, I just, I think, again, it's the evolution of the space, like software engineering, intelligence, yeah, the intelligence side of it.
00:25:13.067 --> 00:25:20.096
Some of it is I have an insight and I'm delivering that insight to a manager so they can do their job better.
00:25:20.616 --> 00:25:32.267
But the other part of that intelligence side is, I have an insight, but I'm actually creating an automation that goes into the SDLC and unblocks one of those blocked areas.
00:25:32.267 --> 00:25:38.217
So for example, at LinearB, if you go into LinearB now, we have a full marketplace of automations.
00:25:38.487 --> 00:25:43.237
Some of those automations help new hires onboard more efficiently.
00:25:43.856 --> 00:25:49.237
Some of them ensure Data integrity or the definition of done of a PR.
00:25:49.267 --> 00:25:57.507
So for example, every PR needs a JIRA ticket, or every PR at least needs to have a definition, or if it's on the front end, like have a screenshot.
00:25:57.517 --> 00:26:00.507
So it's like how, how we work, right?
00:26:01.166 --> 00:26:04.136
Other times I mentioned, cause I just think it's the coolest one.
00:26:04.146 --> 00:26:06.807
Cause now we're finding all these bots are creating code.
00:26:06.987 --> 00:26:13.767
Some of these automations say, Hey, let's look at this Dependabot and say, how risky is this code change?
00:26:14.196 --> 00:26:17.767
And if it's really not risky at all, it's like a minor, minor change.
00:26:17.826 --> 00:26:21.557
Let's not ruin the developer experience, pull them away from coding.
00:26:22.547 --> 00:26:23.317
Let's not do that.
00:26:23.317 --> 00:26:26.326
In that situation, if all the tests pass, let's approve it.
00:26:26.626 --> 00:26:30.136
And yeah, if it's a major version bump, let's pull a developer.
00:26:30.136 --> 00:26:30.376
So it.
00:26:30.731 --> 00:26:33.412
You can kind of think of this automation marketplaces.
00:26:34.142 --> 00:26:34.461
Yes.
00:26:34.471 --> 00:26:42.291
Step one of SEI was data that shows where the bottlenecks are, but really where we've took the industry is.
00:26:42.836 --> 00:26:45.126
Automation to unlock those bottlenecks.
00:26:45.126 --> 00:26:49.366
And I think that's maybe, that's where I believe that the space is going.
00:26:49.366 --> 00:26:52.926
Delivery of intelligence into your SDLC and to your managers.
00:26:53.386 --> 00:27:07.856
So unlike something like GitHub Actions, the automations within LinearB's SEI platform are actually Helping you both see the insight on the metric side and then directly relate an action to it and kind of roll it out to your org.
00:27:08.416 --> 00:27:10.626
Yeah, I think the difference is, is like you need both.
00:27:10.656 --> 00:27:19.497
If you're a platform engineering team or you're the head of developer experience, the first thing that you need to know is where do I focus my time to help all the developers?
00:27:20.106 --> 00:27:24.247
So when you log into an SEI platform, you log into our platform, it shows you that.
00:27:24.642 --> 00:27:26.281
Here's where you focus your time.
00:27:27.362 --> 00:27:32.832
Now that you know where the bottleneck is or where that bad experience is, it's like, what are you going to do about it?
00:27:32.942 --> 00:27:36.152
Oh, go check out the automation library.
00:27:36.622 --> 00:27:39.731
Here's all these automation that other customers are using.
00:27:39.761 --> 00:27:42.582
Other people wrote, already solved your problem.
00:27:43.132 --> 00:27:46.981
Load balancing reviews, for example, finding an expert reviewer.
00:27:47.652 --> 00:27:55.659
Uh, creating, automatically creating a JIRA ticket when, there's a vulnerability found, there's people that have already done it.
00:27:55.669 --> 00:27:56.709
So it's like, Oh, okay.
00:27:56.709 --> 00:28:00.608
I can almost like, just like drag and drop and say like, this is going to help me.
00:28:00.969 --> 00:28:02.808
And I think that, that's the difference.
00:28:04.078 --> 00:28:09.009
And this relates back to that predictability element you mentioned earlier as well as dev experience.
00:28:09.009 --> 00:28:18.449
Where it's like, okay, we're helping improve the experience and kind of streamline it so we can better understand, you know, where are there actual challenges and when will we actually deliver code?
00:28:18.449 --> 00:28:21.348
I think, I think it all, all relates together.
00:28:21.348 --> 00:28:26.219
Like, if you think predictability, we usually look at two metrics, like planning accuracy.
00:28:26.804 --> 00:28:28.104
Capacity, Accuracy.
00:28:28.384 --> 00:28:31.074
Now we've added in a forecasting.
00:28:31.074 --> 00:28:33.763
When will the, what day will the project be delivered on?
00:28:33.763 --> 00:28:36.703
We're using a Monte Carlo simulation to do so.
00:28:37.364 --> 00:28:41.574
But if you look at all of that stuff, it's like, what affects the delivery date?
00:28:42.114 --> 00:28:47.124
Well, the experience of the developer, are there bottlenecks in the, in the process?
00:28:47.804 --> 00:28:50.384
You can still think of some of your classic DORA metrics.
00:28:50.384 --> 00:28:52.693
Like, are they getting pulled away to production?
00:28:52.703 --> 00:28:54.483
Cause your change failure rate is too high.
00:28:55.098 --> 00:29:01.269
Are there too many bugs coming into the project unexpectedly, which will lower your planning accuracy?
00:29:01.878 --> 00:29:04.509
And it all relates together for on time delivery.
00:29:04.538 --> 00:29:08.808
So it's kind of like, if you improve productivity, you'll improve the experience.
00:29:08.808 --> 00:29:11.759
If you improve the experience, you'll improve on time delivery.
00:29:11.759 --> 00:29:14.654
If you improve on time delivery, you'll meet your business goals.
00:29:15.584 --> 00:29:24.784
And now my software engineering intelligence platform was worthwhile for me to roll out as a, as a buyer of this.
00:29:24.923 --> 00:29:26.233
CTO, a VP, yeah.
00:29:26.594 --> 00:29:29.153
What about this kind of next stage that we're already seeing?
00:29:29.153 --> 00:29:35.878
So you alluded to Automations to help with bot generated PRs and some of these bot automations we're seeing.
00:29:36.269 --> 00:29:42.078
But, uh, you know, GenAI is rapidly accelerating the amount of code that we're able to generate.
00:29:42.278 --> 00:29:46.108
Uh, it's making some really simple stuff even more simple.
00:29:46.318 --> 00:29:49.368
Uh, it's letting us, you know, code tests a lot faster.
00:29:49.378 --> 00:29:50.719
All these different use cases.
00:29:50.719 --> 00:29:59.564
How is, uh, An SEI platform like LinearB helping to improve the orchestration of Gen AI code within this offer to delivery life cycle.
00:29:59.923 --> 00:30:07.933
Yeah, I think like the way that I think about Gen AI, it's kind of like, what are we doing to help measure it first?
00:30:08.044 --> 00:30:13.023
So like with LinearB, we can measure the impact of your co pilot initiative for your developers.
00:30:13.584 --> 00:30:16.574
We can actually say, did it make us more productive?
00:30:16.574 --> 00:30:17.913
Did it cause more bugs?
00:30:17.913 --> 00:30:19.203
Like what was the outcome of it?
00:30:19.743 --> 00:30:21.134
I think that, I think that's one side of it.
00:30:22.159 --> 00:30:31.449
Then the second side of it is, okay, let's say that we know that Copilot and GenAI for developers is here to stay.
00:30:32.328 --> 00:30:39.288
Now we know that more code is going to be delivered faster, but where do you have the bottleneck now?
00:30:39.439 --> 00:30:42.759
It is usually in that review and deployment process.
00:30:43.959 --> 00:30:50.278
So making that smarter, make better decisions there, understand the risk of the change, put in policy and rules.
00:30:50.278 --> 00:30:51.538
That's what we do with GitStream.
00:30:51.554 --> 00:30:55.044
So it's kind of like help, help smooth it out.
00:30:55.943 --> 00:31:01.913
But then also it's kind of like, well, how are we using GenAI within our platform itself within SEI, right?
00:31:02.594 --> 00:31:08.884
So for example, right now we have a GenAI report for your sprint retrospective.
00:31:08.943 --> 00:31:09.564
Here's the thing.
00:31:09.564 --> 00:31:10.814
There's all this data.
00:31:10.834 --> 00:31:12.003
Think about a manager.
00:31:12.023 --> 00:31:13.604
Think about everyone working on the team.
00:31:13.854 --> 00:31:16.153
There's so much data coming into the platform.
00:31:16.153 --> 00:31:24.459
What GenAI is really good at is like distilling that data and making it human readable to say like, Here's what we saw with the bottlenecks.
00:31:24.479 --> 00:31:26.048
Here's what you should do next.
00:31:26.058 --> 00:31:28.159
Or here's how this sprint went for you.
00:31:28.709 --> 00:31:36.598
Or here's what we see happening within your people in terms of, you know, who is getting bogged down by toil and who's not.
00:31:36.618 --> 00:31:42.719
So it's kind of like distilling that information to something that's more digestible, human readable, and then actionable.
00:31:43.124 --> 00:31:44.943
So those are kind of like the phases that I see.
00:31:45.465 --> 00:31:55.145
Dan, what do you view as helping great SEI platforms stand out from the more copy and paste solutions that people are trying to pitch?
00:31:55.736 --> 00:31:57.326
Yeah, that's a good question.
00:31:57.506 --> 00:31:58.895
And I think it's pretty simple.
00:31:59.516 --> 00:32:05.875
I think a mediocre SEI platform is just giving you metrics.
00:32:06.365 --> 00:32:08.346
Maybe it's just giving you DORA metrics.
00:32:08.346 --> 00:32:08.486
Yeah.
00:32:08.875 --> 00:32:12.445
Maybe it's giving you some project delivery metrics, that type of stuff.
00:32:12.776 --> 00:32:17.306
But at the end of the day, it's giving you the data, but that data is just sitting on the screen.
00:32:18.316 --> 00:32:20.405
What is a great SEI platform?
00:32:20.405 --> 00:32:24.655
And this is why, you know, I like to say with LinearB, we're like SEI plus.
00:32:24.816 --> 00:32:25.915
We're more than just SEI.
00:32:26.865 --> 00:32:33.655
It has to do with the automations that you can deploy that actually help improvement.
00:32:33.715 --> 00:32:40.705
Now that could be improvement with efficiency, That could be improvement in developer experience, that could be improvement with project delivery.
00:32:41.195 --> 00:32:58.875
But a very, like, down to earth example would be, let's say that you log into your environment, and you see a piece of data, and that piece of data says, you have a bottleneck in your pull request review process.
00:32:59.415 --> 00:33:00.516
That's what the data says.
00:33:00.516 --> 00:33:10.701
And you know, a good SEI platform will show you a benchmark, And it will show you how you compare the industry average, and you can set a goal to improve, and all of that.
00:33:11.270 --> 00:33:22.671
But a great SEI platform says, the reason that you have this bottleneck, is we notice that most of the reviews are down to one person per team.
00:33:22.941 --> 00:33:25.401
Like a senior reviewer, and that person's the bottleneck.
00:33:26.260 --> 00:33:47.361
And by the way, we have an automation, a recommended automation, that you could deploy That does a round robin load balancing that will distribute the reviews to your, some of your junior managers, some of your mid, uh, some of your junior developers, some of your mid level developers, and that automation is going to unlock your bottleneck.
00:33:47.451 --> 00:33:54.861
That's a great SEI platform because it went from data to insight, to automated, automated solution.
00:33:55.201 --> 00:34:01.631
We're a mediocre one, or maybe one that's like only for free, or you're collecting it in a spreadsheet, or it's like a lower end one.
00:34:01.961 --> 00:34:04.621
We'll just say, Hey, your cycle time is bad.
00:34:05.074 --> 00:34:05.364
Yeah.
00:34:05.374 --> 00:34:13.514
They're, they're kind of thinking about it with this lens of a few years ago in the industry and not taking on the innovation approach of how do we actually help you orchestrate code?
00:34:13.523 --> 00:34:15.693
How do we actually help you deliver more predictability?
00:34:16.164 --> 00:34:17.963
They're just saying, Oh, we see a problem.
00:34:18.684 --> 00:34:19.123
That's right.
00:34:19.454 --> 00:34:23.393
Do you have any thoughts on kind of the next phase for software engineering intelligence?
00:34:23.393 --> 00:34:32.514
Like what's the new frontier now, as you have started to really automate some of the improvements, uh, get better at pulling in insights, you know, kind of adapt to gen AI.
00:34:32.514 --> 00:34:34.923
What, what's kind of the next horizon?
00:34:35.503 --> 00:34:38.864
I think it's the co pilot for engineering managers, man.
00:34:39.023 --> 00:34:40.213
That's what I think it is.
00:34:40.583 --> 00:34:51.313
And I think like almost every profession is going to have a co pilot for the Co pilot, we know for developers, co pilot for lawyers, co pilot for CFOs.
00:34:51.414 --> 00:34:56.094
I think co pilot for team leaders, engineering managers, directors.
00:34:56.094 --> 00:35:00.494
That middle layer that I talked about is so tough.
00:35:01.014 --> 00:35:04.884
There is so much data flowing at you.
00:35:04.884 --> 00:35:06.534
You're responsible for people.
00:35:06.594 --> 00:35:09.003
You're responsible for project delivery.
00:35:09.253 --> 00:35:11.454
You're responsible for high quality code.
00:35:11.759 --> 00:35:13.809
You're responsible for an effective workflow.
00:35:14.358 --> 00:35:24.048
But now imagine every morning you wake up and your co pilot is sending you information, probably over Slack, that just says, Hey, here's the three areas to look at today.
00:35:24.048 --> 00:35:26.818
I've already monitored what was going on.
00:35:27.059 --> 00:35:29.108
You might want to have a one on one with this person.
00:35:30.099 --> 00:35:38.465
You could improve your workflow by, uh, deploying this, code expert automation to find the right reviewer for some load balancing.
00:35:38.465 --> 00:35:38.516
Bye.
00:35:39.425 --> 00:35:43.996
And also over here, there's a few sensitive code changes that you might want to look at.
00:35:44.326 --> 00:35:46.905
I know you can't review everything, but you might want to look at this.
00:35:47.346 --> 00:35:50.646
Wow, now I'm like 20 percent more effective.
00:35:51.005 --> 00:35:52.485
That's where I see it going.
00:35:53.565 --> 00:35:54.775
And that's what we're working on.
00:35:55.146 --> 00:36:06.436
So LinearB aggregates qualitative and quantitative data for software engineering leaders to help them make better decisions and remove these workflow bottlenecks with automations and other opportunities.
00:36:06.806 --> 00:36:15.726
And this kind of co pilot system, it can simply continue to be enhanced with, you know, new automations, new data sources.
00:36:16.210 --> 00:36:19.851
Uh, new you know, AI'd Opportunities, that's kind of what he's saying.
00:36:19.851 --> 00:36:23.280
Connect to your calendar, see how much focus time is happening for your developers.
00:36:23.661 --> 00:36:27.460
Hey, you know, on, on Friday I have time to look at some insights.
00:36:27.471 --> 00:36:30.871
Imagine, you know, what, I mean, we have customers doing this now.
00:36:30.920 --> 00:36:40.731
An insight that comes in and says, Hey Conor, just notice like for your team, the amount of focus time that your developers got last week was down 15%.
00:36:41.181 --> 00:36:42.960
First of all, you're probably going to hear this Monday.
00:36:43.001 --> 00:36:44.311
Now you're already ahead of the game.
00:36:44.900 --> 00:36:45.161
Yeah.
00:36:45.201 --> 00:36:45.731
Oh, okay.
00:36:45.731 --> 00:36:47.731
Hey, now imagine coming in Monday morning.
00:36:47.731 --> 00:36:49.280
Hey, I know that we didn't get as much.
00:36:49.311 --> 00:36:50.871
We had too many meetings last week.
00:36:50.911 --> 00:36:51.710
I know that.
00:36:52.371 --> 00:36:58.561
Um, let's talk about which ones we need to remove so that everyone can focus on developing this week.
00:36:58.971 --> 00:37:00.481
That's a good manager.
00:37:01.300 --> 00:37:07.371
A bad manager is you find out two weeks later, but all your developers know they've been complaining.
00:37:07.400 --> 00:37:08.721
You see, see the difference?
00:37:09.161 --> 00:37:09.440
Yeah.
00:37:09.471 --> 00:37:13.811
That's where I think we can just like excel, accelerate, make you a better manager.
00:37:14.186 --> 00:37:16.856
That makes total sense.
00:37:17.326 --> 00:37:20.155
Do you have any other thoughts around SEI you want to share with the audience?
00:37:20.246 --> 00:37:24.436
This has been a great dive into it, but I wonder if there's other areas you want to dive into.
00:37:24.485 --> 00:37:29.155
First of all, like, I'm happy to say that the space is here.
00:37:29.255 --> 00:37:30.545
It is here to stay.
00:37:30.646 --> 00:37:38.485
I think you should look into it so that you're, you know, whether we say by 2027, 50%, 50 percent growth, something like that.
00:37:38.485 --> 00:37:39.456
Look into it now.
00:37:39.981 --> 00:37:41.260
Make sure you're on top of it.
00:37:41.701 --> 00:37:55.141
And then if you are on top of it, make sure that you're aligning it to initiatives and using automations for your people, like the co pilot thing that I talked about, the insights are coming to your people, and then make sure that you're automating your SDLC.
00:37:55.141 --> 00:37:56.291
You're doing those things.
00:37:56.291 --> 00:37:57.621
You're ahead of the game.
00:37:57.621 --> 00:37:57.675
You're doing it.
00:37:59.106 --> 00:38:00.615
That's what I'd love to see everybody do.
00:38:01.126 --> 00:38:01.416
Yeah.
00:38:01.416 --> 00:38:08.695
As Dan said, Gartner believes that by 2027, 50 percent of engineering teams will have adopted software engineering intelligence.
00:38:08.786 --> 00:38:22.996
So if you're one of those teams that is looking to evaluate SEI platforms or better understand the space, I know LinearB has created a free essential guide to SEI, uh, to help you explore those platforms and you can download it at LinearB.
00:38:22.996 --> 00:38:24.056
io slash resources.
00:38:24.126 --> 00:38:26.686
We'll also include a link, uh, in the show nights here.
00:38:27.141 --> 00:38:29.900
Um, Dan, thank you so much for this great conversation and thanks everyone for tuning in.
00:38:30.443 --> 00:38:31.092
Thanks, Conor.
00:00:00.000 --> 00:00:02.810
You're getting pressure to deliver projects on time.
00:00:03.290 --> 00:00:12.919
You're getting pressure to take, the millions of dollars in workforce that you have and ensure they're working as efficiently as possible and then report that back to the board.
00:00:13.720 --> 00:00:14.050
Right.
00:00:14.394 --> 00:00:24.785
You're getting pressure to make sure that the developer's experience is fantastic, because these folks can work anywhere they want in high demand, right?
00:00:25.274 --> 00:00:34.865
So if you're like the VP I think software engineering intelligence helps you to push one of your initiatives that would have been very difficult to do without data.
00:00:35.021 --> 00:00:45.222
Gardener just released their market guide, showing that software engineering intelligence platforms, help engineering leaders significantly improve both team productivity and value delivery.
00:00:46.002 --> 00:00:51.942
Through Gardner's in-depth analysis on the critical features of sci platforms and how they can be used to drive engineering excellence.
00:00:52.222 --> 00:00:54.531
Linear B was named as a representative vendor.
00:00:54.951 --> 00:00:59.316
And therefore we're giving away a complimentary copy of Gartner's sci market guide.
00:00:59.514 --> 00:01:08.243
Head to the link in the show notes to download your complimentary copy and learn how you can unlock the transformative potential of software engineering intelligence for your team.
00:01:09.179 --> 00:01:10.799
Hey, what's up everyone?
00:01:10.799 --> 00:01:15.569
We're back with another Labs episode of Dev Interrupted.
00:01:15.748 --> 00:01:20.799
I'm your host and linear BCOO and Co-founder Dan Lyons.
00:01:21.129 --> 00:01:25.209
Today I'll be joined by my co-host, Connor B.
00:01:25.718 --> 00:01:27.638
Great to see you as always, Connor.
00:01:27.989 --> 00:01:28.828
Likewise, Dan.
00:01:28.828 --> 00:01:32.608
Always great to be here and to hear about how badly your fantasy baseball team is doing.
00:01:32.805 --> 00:01:36.195
I know we were just talking before, we're at five and six.
00:01:36.801 --> 00:01:39.340
We're battling for a playoff spot.
00:01:39.680 --> 00:01:41.490
You and I do fantasy football together.
00:01:41.501 --> 00:01:43.141
I think fantasy football is my number one.
00:01:43.141 --> 00:01:49.131
Fantasy baseball is more like, tie me over until football season starts, but definitely.
00:01:49.581 --> 00:01:50.980
Learning a lot about baseball.
00:01:51.311 --> 00:01:57.450
What I love is that you, not only in your career, but in your personal life, just, you love data.
00:01:57.471 --> 00:02:02.921
You're like, Hey, I really want to spend my free time figuring out data around a sport.
00:02:03.290 --> 00:02:06.990
Uh, you know, in my work life, you know, let's do data around engineering teams.
00:02:07.001 --> 00:02:08.561
How can I optimize these teams?
00:02:08.561 --> 00:02:09.251
They perform better.
00:02:09.251 --> 00:02:11.800
You know, it's almost like you have an obsession here or something.
00:02:12.320 --> 00:02:13.170
That's actually funny.
00:02:13.170 --> 00:02:17.691
I think, honestly, probably a lot of our audience has seen Moneyball.
00:02:18.131 --> 00:02:22.121
Moneyball is like what turned me on to fantasy baseball.
00:02:22.610 --> 00:02:22.850
Mm-Hmm.
00:02:23.600 --> 00:02:23.721
Yeah.
00:02:23.721 --> 00:02:24.170
Checking the
00:02:24.170 --> 00:02:26.540
data, looking at the box score.
00:02:27.050 --> 00:02:30.170
Where's the optimization we can find people aren't necessarily seeing so far.
00:02:30.230 --> 00:02:30.561
Yeah.
00:02:30.770 --> 00:02:32.691
A little nerdy, but yeah, I like it.
00:02:33.401 --> 00:02:33.580
Um,
00:02:33.580 --> 00:02:39.401
well, and I think that drives into kind of the things that we talk about with dev, developer productivity and developer experience, like.
00:02:39.901 --> 00:02:45.501
What are the, the workflow challenges that we can find, or what are the things that people are underrating?
00:02:45.570 --> 00:02:51.281
You know, slugging percentage, maybe for an example for baseball, that people aren't rating as highly as they need to.
00:02:51.681 --> 00:02:58.241
Um, our strikeouts a big issue, or you know, our, our long PR is a big issue, like these kind of optimizations.
00:02:59.091 --> 00:03:09.401
Yeah, I think the cool thing is like, Let's say that you're observing baseball or like you're observing how your engineering team works and you might have like an intuition or a theory.
00:03:09.401 --> 00:03:12.070
This happens all the time, both in business and sports.
00:03:12.080 --> 00:03:13.420
Like I'm watching a game.
00:03:13.841 --> 00:03:17.591
I, and, and sometimes I'm proven right and sometimes I'm proving wrong.
00:03:17.591 --> 00:03:19.480
Like I think a player is getting better.
00:03:19.980 --> 00:03:24.110
Actually, like the underlying data says that's not the case or vice versa.
00:03:24.110 --> 00:03:24.491
Like.
00:03:25.010 --> 00:03:30.691
Something on my, uh, in business is like, Oh, I think we might have a problem in this area of the workflow.
00:03:30.741 --> 00:03:32.311
Actually, the data says we're okay.
00:03:32.311 --> 00:03:33.341
It's somewhere else.
00:03:33.350 --> 00:03:35.631
So I think that's also cool to see like that.
00:03:36.170 --> 00:03:37.691
Hey, this is what I think's happening.
00:03:37.691 --> 00:03:39.320
What's the data show me that type of thing.
00:03:39.320 --> 00:03:41.781
So yeah, maybe that's where the fantasy, uh, is.
00:03:42.091 --> 00:03:43.711
Uh, Sports Addiction comes from.
00:03:44.371 --> 00:03:47.001
Well, and I know that's the topic for what you want to chat about today, right?
00:03:47.001 --> 00:03:52.540
Which is, you know, how are we understanding the problems and then solving them that are happening in software engineering?
00:03:53.170 --> 00:04:08.300
Yeah, I think we have a cool topic today And I'll start out I think by just saying earlier this year Gartner released their debut SEI platform market guide.
00:04:08.300 --> 00:04:13.110
I'm sure most people on the pod know what You What's up with Gartner?
00:04:13.800 --> 00:04:35.940
And I think it's really important because finally, kind of like all this stuff that we've been talking about with data, maybe since the beginning of the pod has come to fruition that it actually has like a formal name and is recognized and it's this thing it's called SEI and There's a lot of love for it.
00:04:36.380 --> 00:04:53.490
Gartner estimates that adoption of SEI platforms, they're poised to increase about 50 percent of engineering teams will have one, uh, one of these SEI solutions by 2027 compared to only a 5 percent increase this year.
00:04:53.490 --> 00:04:56.110
So it's like hot topic picking up.
00:04:56.461 --> 00:04:58.420
That's where we're going to dive in today.
00:04:58.670 --> 00:05:02.120
What is up with this SEI stuff?
00:05:02.875 --> 00:05:17.406
Yeah, so maybe we can start with the basics, Dan, since I know we talk about a lot of these kind of areas, these initiatives, uh, on the podcast, but, uh, what is software engineering intelligence and what is a software engineering intelligence or SEI platform?
00:05:17.935 --> 00:05:20.786
Yeah, well, we probably should have said that first, right?
00:05:20.826 --> 00:05:32.216
SEI, Software Engineering Intelligence, that's what it stands for, but actually it's kind of evolved since I've been this and you've been doing this six, seven, eight years now.
00:05:32.740 --> 00:05:37.891
I would say the first incarnation of it, I would describe it something like this.
00:05:37.951 --> 00:05:38.370
Okay.
00:05:38.420 --> 00:05:47.161
SEI, it's this data driven visibility and insights into how engineering teams operate and improve their efficiency.
00:05:47.161 --> 00:05:50.540
That's maybe how I would talk about it like five years ago.
00:05:51.261 --> 00:05:56.331
And it's not that it's wrong, but I actually think the space, uh, has evolved.
00:05:56.350 --> 00:05:57.620
We've all evolved with it.
00:05:58.190 --> 00:05:59.841
And now I would say.
00:06:00.790 --> 00:06:26.300
Actually, it's still about being data driven and providing visibility and providing insights, but more importantly, it's really how our product delivery organizations transforming in a few different areas, like their on time project delivery, the allocation and alignment of their costs, developer coaching, efficiency, dev experience.
00:06:26.800 --> 00:06:29.120
It's really about transforming these.
00:06:29.430 --> 00:06:35.190
I would call them more like, uh, like business value initiatives with data.
00:06:35.451 --> 00:06:44.490
And that's more so where, where I think SEI sits today, which, which is a great evolution from just like data and visibility to like really moving the needle with the business.
00:06:45.471 --> 00:06:59.576
And this lines up with this dual mandate conversation we've been having here for podcast of like, how do we both improve operational efficiency within engineering orgs and then align that engineering effort with business goals.
00:06:59.956 --> 00:07:09.065
And I know a couple of the key capabilities that we're seeing the, the best software engineering intelligence platforms have are that ability to turn insights into action.
00:07:09.461 --> 00:07:23.100
Via automation or other opportunities, and then that other side of things around how can we understand the investment, the resource allocation that we're making at a team and project level for engineering teams?
00:07:23.507 --> 00:07:38.735
Yeah, I think you're kind of hitting on some of the I don't know, I would say like the core features or core capabilities or maybe the best way to describe it is, let's say that you want to run a software engineering intelligence program at your company.
00:07:39.694 --> 00:07:40.805
Say that you want to do that.
00:07:40.834 --> 00:07:43.074
Okay, you understand the space is heating up.
00:07:43.274 --> 00:07:45.334
You got to get data driven.
00:07:45.714 --> 00:07:47.334
Now you want to start an initiative.
00:07:47.355 --> 00:07:48.454
What do you need to do?
00:07:48.824 --> 00:07:52.214
So maybe let me describe some of the things that make up SEI.
00:07:53.319 --> 00:07:59.839
I think first and foremost, it's around predictable or on time project delivery.
00:08:00.480 --> 00:08:02.579
I'll give like the, the real stories behind that.
00:08:02.579 --> 00:08:05.370
I guess that's the other thing I like about this SCI space now.
00:08:05.389 --> 00:08:09.879
Went from just like this, yeah, measure everything with data to like real life stuff.
00:08:09.949 --> 00:08:13.300
Real life stuff is, I'm a VP of engineering.
00:08:13.310 --> 00:08:16.370
The number one question that I get asked is, when is the project going to be done?
00:08:16.680 --> 00:08:17.769
When's the feature coming out?
00:08:18.584 --> 00:08:20.685
That's what I'm being asked by my CEO.
00:08:20.714 --> 00:08:23.105
That's what I'm being asked by the sales team.
00:08:23.105 --> 00:08:25.095
That's what I'm being asked by the product team.
00:08:25.845 --> 00:08:30.564
So the first core module of SEI is what I call on time project delivery.
00:08:31.204 --> 00:08:32.985
That's probably my favorite one.
00:08:32.985 --> 00:08:34.375
Now there's a bunch of other ones.
00:08:34.804 --> 00:08:37.825
I'll list them all out, but maybe, maybe that's the first one.
00:08:38.174 --> 00:08:43.705
The other ones are around profitable engineering, resource alignment.
00:08:43.774 --> 00:08:45.884
Are we investing into the right things?
00:08:47.034 --> 00:08:51.625
Where's the money going into our projects and people like that's super important.
00:08:51.634 --> 00:09:01.337
Again, it's tied back to the business, not just metrics, but it's like CEO comes to you and says, Hey, I'm giving you a few million dollars, right.
00:09:01.337 --> 00:09:03.687
To staff your development team.
00:09:04.427 --> 00:09:05.837
Where is that money going?
00:09:05.837 --> 00:09:07.508
And what's the outcome for our business?
00:09:08.268 --> 00:09:08.548
Right.
00:09:08.692 --> 00:09:08.982
Yeah.
00:09:08.982 --> 00:09:15.062
So, so you mentioned this phrase, profitable engineering, and I, I've started to hear that now in the industry lately.
00:09:15.293 --> 00:09:21.403
What does that mean to you and why should engineering leaders be thinking about engineering profitability?
00:09:21.816 --> 00:09:23.535
First of all, I think it sounds really cool.
00:09:23.566 --> 00:09:24.846
Profitable engineering.
00:09:25.046 --> 00:09:26.436
It's catchy, right?
00:09:26.865 --> 00:09:27.186
Absolutely.
00:09:27.436 --> 00:09:29.946
Let's like do the non catchy version.
00:09:30.225 --> 00:09:41.682
I think the non catchy version is something like, Take your project costs, so the cost that it takes to develop every project that you're working on, okay?
00:09:42.503 --> 00:09:49.832
And do a translation of that cost back to your business folks.
00:09:50.283 --> 00:09:55.253
Again, could be CEO, could be anyone on the executive team, could be your board.
00:09:56.293 --> 00:10:00.773
And justify that you should be investing in project A.
00:10:01.482 --> 00:10:09.373
And you should be investing, let's say 25 percent of your engineering workforce, which may be millions of dollars if you're a bigger company.
00:10:11.143 --> 00:10:15.352
And then how can you translate to say, this is how I think it will help the business.
00:10:15.373 --> 00:10:23.192
That's how I think about profitable engineering and Conor, what I would say, it's like so important actually for software engineering intelligence.
00:10:23.212 --> 00:10:29.357
Cause again, if you're just looking at metrics of like cycle time, not saying cycle time's bad or something like that.
00:10:29.798 --> 00:10:38.528
But if you're just looking at like a cycle time metric, you're pretty far away from talking about how this affects your business, right?
00:10:38.977 --> 00:10:48.727
And with these things like on time project delivery, profitable engineering, I think it kind of elevates your career and like elevates the engineering team's importance to the business.
00:10:48.727 --> 00:10:49.878
And that's what I like about it.
00:10:50.798 --> 00:10:57.238
So why do you think these software engineering intelligence platforms are coming to the forefront now?
00:10:58.227 --> 00:11:00.437
Yeah, no, that, that's an awesome question.
00:11:00.437 --> 00:11:06.368
I actually thought a lot about it over the last few weeks, cause that's what I do in my spare time.
00:11:06.368 --> 00:11:12.038
And I don't think that there's one reason, but there's a collection of reasons.
00:11:12.118 --> 00:11:28.942
The first thing is, I think there was like this collective consciousness that everyone at a similar time within engineering and also the executive team said, Hey, I really think data can help us.
00:11:29.153 --> 00:11:36.452
Like if we visualize the data of how engineering works, how product engineering works, I think it can help us.
00:11:37.212 --> 00:11:43.363
And the reason I think it all happened at the same time or similar time is because you saw that in all different areas.
00:11:43.363 --> 00:11:45.832
Like sales has data, marketing has data.
00:11:45.852 --> 00:11:47.113
They've had it for a long time.
00:11:48.013 --> 00:11:55.243
But even like, uh, HR has data now, People Analytics, Engagement Survey, so that, that's like one thing that happened.
00:11:55.543 --> 00:12:02.293
Then the second thing that happened is, our ability to collect this data is easier than it's ever been.
00:12:02.293 --> 00:12:09.873
If you think about LinearB, we're connected into your Git environment, we're connected into your project environment, we're connected into your chat.
00:12:10.982 --> 00:12:12.712
We can see all of your releases.
00:12:12.712 --> 00:12:14.373
We can see everything with CICB.
00:12:14.633 --> 00:12:17.373
We can see all the tooling that you're using in the cloud.
00:12:17.373 --> 00:12:17.682
Sneak.
00:12:17.682 --> 00:12:22.263
So it's like not only did everyone say, Hey, I think this data would be useful.
00:12:22.852 --> 00:12:25.033
We can actually get the data pretty easily.
00:12:25.033 --> 00:12:28.143
Now you can get LinearB up and running with a few clicks of a button.
00:12:28.143 --> 00:12:29.903
That wasn't possible a long time ago.
00:12:30.623 --> 00:12:31.052
All right.
00:12:31.312 --> 00:12:32.673
So I think that's the second reason.
00:12:33.092 --> 00:12:37.113
Then I think these two other ones are more like what's happening right now.
00:12:37.962 --> 00:12:41.187
Every company is a software company now.
00:12:41.715 --> 00:12:49.365
You have companies that sell, I won't say the company's names, but you have companies that sell burgers, cheeseburgers.
00:12:50.644 --> 00:12:51.815
They're software companies.
00:12:51.815 --> 00:12:54.455
You order your burgers on your mobile app.
00:12:54.465 --> 00:12:58.075
They're getting more mobile app orders than go up to the counter orders.
00:12:58.245 --> 00:13:00.044
They have thousands of developers working.
00:13:01.054 --> 00:13:03.845
You have companies that sell tractors.
00:13:05.054 --> 00:13:14.929
Actually, there's a ton of software going into how, how do you, um, Look at all of this equipment and see what's working and what's not working.
00:13:14.940 --> 00:13:23.750
How do you maintain agriculture using drones and all this like insane technology, soft farming, farming is like software now.
00:13:24.580 --> 00:13:34.759
So it's like, I think the third thing is almost every big company is a software company now, and they have recognized that and they have a large development team.
00:13:35.490 --> 00:13:43.440
Classic example of this is like Domino's, for example, where it's been this exemplar of like, hey, they became a tech company and transformed to take on this new stage.
00:13:43.809 --> 00:13:45.789
Yeah, but to your point, everyone's doing it now.
00:13:46.169 --> 00:13:47.690
Software has eaten the world.
00:13:48.289 --> 00:13:50.580
So I think, I think that's the third reason.
00:13:50.690 --> 00:13:53.350
And then the fourth reason, I think this is the kicker.
00:13:54.019 --> 00:13:56.509
I do think the AI movement is helping a lot.
00:13:56.711 --> 00:14:11.410
we're working with some companies where 25 percent or more, 40%, 50 percent of the code, specifically pull requests, are raised by bots, not raised up by humans.
00:14:11.500 --> 00:14:11.931
By bots.
00:14:12.010 --> 00:14:12.350
Wow.
00:14:12.980 --> 00:14:22.336
And people want to measure not only the impact of things like Copilot, like is this helping, but also how do I improve the developer experience?
00:14:22.365 --> 00:14:29.855
Because we have so much code coming into our system now that's not necessarily human generated, like what are we going to do to improve?
00:14:30.225 --> 00:14:38.355
And I think if you put those four things together, that's why the space is now mandatory, let's say, or exploding.
00:14:38.820 --> 00:14:45.509
Do you think that kind of every level of an engineering team can benefit from the software engineering intelligence solutions?
00:14:45.980 --> 00:14:49.520
Yeah, I do, but I think, I think that they do it in different ways.
00:14:49.571 --> 00:14:56.130
So the short answer is yes, but it's like different depending on where you work, right, in the org structure.
00:14:56.910 --> 00:15:08.061
So if I'm a dev versus, you know, maybe like a director level, manager level person versus like the VPE, what are the different ways that those personas are going to benefit from SEI?
00:15:08.620 --> 00:15:19.630
So if you're like VPE or CTO, I honestly think the best way that SEI helps you, it's to push one of those initiatives that I just talked about.
00:15:19.640 --> 00:15:22.671
Like you're getting pressure to deliver projects on time.
00:15:23.150 --> 00:15:32.780
You're getting pressure to take, the millions of dollars in workforce that you have and ensure they're working as efficiently as possible and then report that back to the board.
00:15:33.581 --> 00:15:33.910
Right.
00:15:34.255 --> 00:15:44.645
You're getting pressure to make sure that the developer's experience is fantastic, because these folks can work anywhere they want in high demand, right?
00:15:45.135 --> 00:15:54.725
So if you're like the VP I think software engineering intelligence helps you to push one of your initiatives that would have been very difficult to do without data.
00:15:55.082 --> 00:15:59.533
Now if I keep going, because I think you asked then about like the manager or the director?
00:15:59.783 --> 00:16:04.033
Yeah, the person who's maybe managing the platform engineering team or managing a team of devs.
00:16:04.837 --> 00:16:06.967
I think it's one of the hardest jobs in engineering.
00:16:07.587 --> 00:16:13.488
We have a bunch of, I think, earlier episodes, like being an engineering team leader, being a group manager.
00:16:13.498 --> 00:16:16.618
I think that's the hardest job, this middle management layer.
00:16:16.628 --> 00:16:28.927
I think it's that for a lot of industries, but definitely in engineering, you're doing this combination of decision making and managing people, but also, uh, delivering on sprint predictability.
00:16:28.927 --> 00:16:32.008
And you have to be technical, but you also have to have the soft skills.
00:16:32.008 --> 00:16:33.457
There's a lot coming at you.
00:16:34.732 --> 00:16:41.082
And for example, what we're doing at LinearB, you can think about it as like a co pilot for engineering managers.
00:16:41.113 --> 00:16:47.982
Deliver these people insights so that they can be, you know, developers are always like complaining, I have a terrible manager.
00:16:48.482 --> 00:16:51.822
It's because 50 percent of the time they probably do have a terrible manager.
00:16:51.832 --> 00:16:52.743
It's really hard.
00:16:53.302 --> 00:17:04.482
But if we can get this data into the hands of each one of these decision makers, a lot of them are new, by the way, I've been, maybe I've been doing this like two years, one year, they can make better decisions, right?
00:17:04.867 --> 00:17:09.958
So I think like, uh, at the management level, it's like day to day decision making with data.
00:17:09.968 --> 00:17:10.917
It's supercharged that.
00:17:11.028 --> 00:17:12.448
Helping coach your devs too.
00:17:12.897 --> 00:17:13.228
Yeah.
00:17:13.268 --> 00:17:13.748
Oh yeah.
00:17:13.748 --> 00:17:14.157
Of course.
00:17:14.178 --> 00:17:14.718
Of course.
00:17:14.718 --> 00:17:21.855
Like I'm, I'm now responsible for coaching, uh, 15 devs or whatever it is, eight to 15 devs, maybe more.
00:17:22.998 --> 00:17:33.617
And maybe I didn't even mention that as one of the initiatives, but definitely having a developer coaching program that is more data driven and fair for developers.
00:17:33.617 --> 00:17:34.938
How do I grow my career?
00:17:35.067 --> 00:17:37.127
Am I contributing to the team in the right way?
00:17:37.137 --> 00:17:40.657
Am I contributing to pull request reviews, or am I only coding?
00:17:41.688 --> 00:17:47.357
That's definitely an initiative that a lot of the VPs are pushing, and it's hard on managers.
00:17:48.238 --> 00:17:58.147
It's interesting because a software engineering intelligence can really help engineering teams navigate change, whether that's on the personal front, where, hey, I'm trying to, you know, improve my career.
00:17:58.147 --> 00:17:59.218
I'm trying to level up.
00:17:59.218 --> 00:18:01.268
I'm trying to move to a new role.
00:18:01.518 --> 00:18:04.857
Uh, let me get quantitative and qualitative data, and then automations.
00:18:05.182 --> 00:18:11.833
And other unblocking opportunities through the platform that can help me to improve as an individual or as a team leader.
00:18:12.363 --> 00:18:16.032
And that it really helps manage, let's say we acquired a new company.
00:18:16.298 --> 00:18:22.417
Uh, how can we now evaluate how they're integrating with, uh, the rest of our engineering or the rest of our product org?
00:18:22.788 --> 00:18:24.998
Um, I mean, there's a ton of examples of this.
00:18:25.127 --> 00:18:36.928
We go on, but like this ability to better navigate change, uh, is so crucial to resiliency for software engineering teams and, uh, not only can it help you scale, but it can also help you navigate a lot of these other challenges.
00:18:37.478 --> 00:18:43.137
Well, just like a real, real life example, a real life, real life example, we're working with a company right now.
00:18:44.028 --> 00:18:49.248
And I think everyone listening, if you're not a developer, you can remember what it was like to be a developer.
00:18:49.317 --> 00:18:53.228
And actually, the career ladder is a little bit vague.
00:18:53.238 --> 00:18:57.278
Like, what's the difference between a junior developer and a mid level developer?
00:18:57.988 --> 00:19:00.597
Or a mid level developer and a senior developer?
00:19:00.597 --> 00:19:03.317
Or a senior developer and a principal developer?
00:19:04.087 --> 00:19:06.968
In a lot of organizations, it's not defined that well.
00:19:07.417 --> 00:19:18.117
And sometimes, in order to get promoted, it's the type of person that can have A highly articulated conversation with their manager and push for their career and all of that.
00:19:18.397 --> 00:19:21.008
Now the company we're working with, they want to make it more fair.
00:19:21.008 --> 00:19:22.728
There's all types of developers, right?
00:19:23.298 --> 00:19:31.137
Not every developer is like an orator or maybe come on a podcast and can like prove their case of why they should be a senior engineer.
00:19:31.137 --> 00:19:39.472
Now they're saying, hey, with data, we just want to show the capabilities that it takes to be junior, mid, Senior and Principal.
00:19:39.623 --> 00:19:45.853
And for them, for example, one of the big things that they're pushing is how do you contribute to helping other teammates?
00:19:45.913 --> 00:19:57.742
And they're doing that by saying each week, how often are you providing feedback on poll requests and giving comments and helping others and not just doing your own work?
00:19:58.663 --> 00:20:00.732
And they can see that with the data.
00:20:00.732 --> 00:20:10.262
So now when they have that one on one conversation, it's not like you only get promoted from that, but you could say, Hey, Conor, yeah, I can really see that you are helping a lot, actually.
00:20:10.262 --> 00:20:16.573
It picked up from like almost zero, you know, reviews that they're helping with or one per week to like two to three.
00:20:16.623 --> 00:20:17.742
Can we talk about that?
00:20:17.782 --> 00:20:18.863
I saw that out of you.
00:20:18.863 --> 00:20:19.712
That's awesome, man.
00:20:20.268 --> 00:20:22.117
So it's really like career progression.
00:20:22.570 --> 00:20:30.724
Really interesting, and it sparks something for me on the platform side of things because you talked a bit earlier about how this space has evolved.
00:20:30.734 --> 00:20:42.835
You know, it used to just be kind of, oh, let's get some basic engineering metrics so we can understand what's happening, to now really providing these coaching opportunities, uh, improving developer experience and improving developer productivity, some of these big initiatives I want to talk more about.
00:20:43.434 --> 00:20:53.384
But there is also other concept that we've talked a lot about on this pod that I think relates to SEI and I want to understand how you see them as differentiated or aligned, which is value stream management.
00:20:53.694 --> 00:21:02.055
You talked a bit earlier about predictability in the software engineering process in this and trying to bring that predictability and profitability to your software development lifecycle.
00:21:02.414 --> 00:21:10.404
How do you see this concept of software engineering intelligence and what platforms that provide SEI can deliver relating to value stream management?
00:21:10.708 --> 00:21:21.093
So when I When we first founded LinearB like six, seven years ago, there was this concept of VSM, Value Stream Management.
00:21:21.393 --> 00:21:38.823
And there was a big promise there of, I'm going to take every piece of value from you're thinking about a piece of value to you have an idea about it, to you put it onto paper and I'm going to track it all the way out to the customer, getting it.
00:21:39.218 --> 00:21:42.798
Then I'm going to track it all the way around to renewals.
00:21:42.807 --> 00:21:47.657
And I'm going to therefore manage my value stream and we're going to get better.
00:21:49.147 --> 00:22:03.057
And unfortunately, what I've seen with a lot of VSM is the actual rollout and implementation of what I just said is extremely difficult and therefore.
00:22:03.712 --> 00:22:09.383
It's an amazing idea in concept, but I haven't seen it actually implemented very well.
00:22:10.042 --> 00:22:23.303
Now with software engineering intelligence, if you think of VSM the way that I just described it from like I have an idea all the way to like a customer getting value and renewal, I think what's great about SEI is it's more down to earth.
00:22:23.472 --> 00:22:24.903
It fits in the middle of that.
00:22:25.313 --> 00:22:27.752
It's four product delivery teams.
00:22:27.752 --> 00:22:30.883
It's kind of like in the middle of that whole VSM end to end.
00:22:31.407 --> 00:22:36.837
And you can roll it out and you can get all the data for it and you can actually improve.
00:22:36.837 --> 00:22:38.167
So I don't know.
00:22:38.178 --> 00:22:44.498
I'm not trying to say one's better than the other, but I think like SEI became, I would say more down to earth and achievable.
00:22:45.178 --> 00:22:46.508
And that's the big difference.
00:22:46.893 --> 00:22:54.242
So maybe SEI platforms provide an opportunity to actually realize the value of ESM and say, okay, how do we implement this?
00:22:54.242 --> 00:22:57.012
How do we actually put this into practice?
00:22:57.313 --> 00:22:57.903
I think so.
00:22:58.239 --> 00:23:03.588
You've mentioned a couple other key initiatives that are common themes or refrains on this show.
00:23:04.259 --> 00:23:11.608
Developer productivity and developer experience as things that SEI platforms can help teams solve or improve.
00:23:11.979 --> 00:23:15.199
How do you see SEI driving developer productivity?
00:23:15.640 --> 00:23:20.779
Well, it's so great because those two things, productivity and experience, go hand in hand.
00:23:20.930 --> 00:23:21.920
That's what I love about it.
00:23:21.920 --> 00:23:24.549
Sometimes I think it's just like the way that you're viewing it.
00:23:25.869 --> 00:23:29.700
Being a developer, at the end of the day, you want to get your code out to prod.
00:23:30.509 --> 00:23:40.210
You want to, a lot, a lot of developers want to see the impact that they make on the business in terms of like customers using the cool stuff that they create.
00:23:41.200 --> 00:23:44.970
And if I just simplify it, it's like, what's preventing me to do that?
00:23:44.970 --> 00:23:50.869
Where's the toil located where it's like more difficult for me to actually get my job done?
00:23:51.480 --> 00:24:02.700
And with SEI, it actually measures the workflow from coding time, to pull request review time, to pick up time, to deployment time, to how long are the tests running?
00:24:02.710 --> 00:24:09.410
So therefore, it's kind of like measuring that experience for the end, for the engineer, what are they going through?
00:24:10.454 --> 00:24:17.164
And when you turn on an SEI platform for an organization, most of the time you will see a bottleneck in that workflow.
00:24:18.065 --> 00:24:33.295
And if you say to yourself, hey, I'm actually going to use some automation, I think that's the other part of the space, let's use automation in order to fix this workflow problem, your developers will be more productive, but they'll also have a better experience.
00:24:34.035 --> 00:24:36.394
So most of the time it goes hand in hand for me.
00:24:36.821 --> 00:24:44.201
It's really interesting you mention automation because I know this is an area where LinearB is taking a different approach to many of the other SEI platforms out there.
00:24:45.402 --> 00:24:50.392
Turning insights into improvement by actually providing automation's built in platform.
00:24:51.132 --> 00:24:59.027
And it's exciting to see That you can now go into the LinearB platform and kind of just add an automation that relates to, Hey, I want to solve this use case.
00:24:59.336 --> 00:25:04.787
Can you explain a bit more about how you view LinearB as going beyond the basics?
00:25:05.297 --> 00:25:13.047
Yeah, I just, I think, again, it's the evolution of the space, like software engineering, intelligence, yeah, the intelligence side of it.
00:25:13.067 --> 00:25:20.096
Some of it is I have an insight and I'm delivering that insight to a manager so they can do their job better.
00:25:20.616 --> 00:25:32.267
But the other part of that intelligence side is, I have an insight, but I'm actually creating an automation that goes into the SDLC and unblocks one of those blocked areas.
00:25:32.267 --> 00:25:38.217
So for example, at LinearB, if you go into LinearB now, we have a full marketplace of automations.
00:25:38.487 --> 00:25:43.237
Some of those automations help new hires onboard more efficiently.
00:25:43.856 --> 00:25:49.237
Some of them ensure Data integrity or the definition of done of a PR.
00:25:49.267 --> 00:25:57.507
So for example, every PR needs a JIRA ticket, or every PR at least needs to have a definition, or if it's on the front end, like have a screenshot.
00:25:57.517 --> 00:26:00.507
So it's like how, how we work, right?
00:26:01.166 --> 00:26:04.136
Other times I mentioned, cause I just think it's the coolest one.
00:26:04.146 --> 00:26:06.807
Cause now we're finding all these bots are creating code.
00:26:06.987 --> 00:26:13.767
Some of these automations say, Hey, let's look at this Dependabot and say, how risky is this code change?
00:26:14.196 --> 00:26:17.767
And if it's really not risky at all, it's like a minor, minor change.
00:26:17.826 --> 00:26:21.557
Let's not ruin the developer experience, pull them away from coding.
00:26:22.547 --> 00:26:23.317
Let's not do that.
00:26:23.317 --> 00:26:26.326
In that situation, if all the tests pass, let's approve it.
00:26:26.626 --> 00:26:30.136
And yeah, if it's a major version bump, let's pull a developer.
00:26:30.136 --> 00:26:30.376
So it.
00:26:30.731 --> 00:26:33.412
You can kind of think of this automation marketplaces.
00:26:34.142 --> 00:26:34.461
Yes.
00:26:34.471 --> 00:26:42.291
Step one of SEI was data that shows where the bottlenecks are, but really where we've took the industry is.
00:26:42.836 --> 00:26:45.126
Automation to unlock those bottlenecks.
00:26:45.126 --> 00:26:49.366
And I think that's maybe, that's where I believe that the space is going.
00:26:49.366 --> 00:26:52.926
Delivery of intelligence into your SDLC and to your managers.
00:26:53.386 --> 00:27:07.856
So unlike something like GitHub Actions, the automations within LinearB's SEI platform are actually Helping you both see the insight on the metric side and then directly relate an action to it and kind of roll it out to your org.
00:27:08.416 --> 00:27:10.626
Yeah, I think the difference is, is like you need both.
00:27:10.656 --> 00:27:19.497
If you're a platform engineering team or you're the head of developer experience, the first thing that you need to know is where do I focus my time to help all the developers?
00:27:20.106 --> 00:27:24.247
So when you log into an SEI platform, you log into our platform, it shows you that.
00:27:24.642 --> 00:27:26.281
Here's where you focus your time.
00:27:27.362 --> 00:27:32.832
Now that you know where the bottleneck is or where that bad experience is, it's like, what are you going to do about it?
00:27:32.942 --> 00:27:36.152
Oh, go check out the automation library.
00:27:36.622 --> 00:27:39.731
Here's all these automation that other customers are using.
00:27:39.761 --> 00:27:42.582
Other people wrote, already solved your problem.
00:27:43.132 --> 00:27:46.981
Load balancing reviews, for example, finding an expert reviewer.
00:27:47.652 --> 00:27:55.659
Uh, creating, automatically creating a JIRA ticket when, there's a vulnerability found, there's people that have already done it.
00:27:55.669 --> 00:27:56.709
So it's like, Oh, okay.
00:27:56.709 --> 00:28:00.608
I can almost like, just like drag and drop and say like, this is going to help me.
00:28:00.969 --> 00:28:02.808
And I think that, that's the difference.
00:28:04.078 --> 00:28:09.009
And this relates back to that predictability element you mentioned earlier as well as dev experience.
00:28:09.009 --> 00:28:18.449
Where it's like, okay, we're helping improve the experience and kind of streamline it so we can better understand, you know, where are there actual challenges and when will we actually deliver code?
00:28:18.449 --> 00:28:21.348
I think, I think it all, all relates together.
00:28:21.348 --> 00:28:26.219
Like, if you think predictability, we usually look at two metrics, like planning accuracy.
00:28:26.804 --> 00:28:28.104
Capacity, Accuracy.
00:28:28.384 --> 00:28:31.074
Now we've added in a forecasting.
00:28:31.074 --> 00:28:33.763
When will the, what day will the project be delivered on?
00:28:33.763 --> 00:28:36.703
We're using a Monte Carlo simulation to do so.
00:28:37.364 --> 00:28:41.574
But if you look at all of that stuff, it's like, what affects the delivery date?
00:28:42.114 --> 00:28:47.124
Well, the experience of the developer, are there bottlenecks in the, in the process?
00:28:47.804 --> 00:28:50.384
You can still think of some of your classic DORA metrics.
00:28:50.384 --> 00:28:52.693
Like, are they getting pulled away to production?
00:28:52.703 --> 00:28:54.483
Cause your change failure rate is too high.
00:28:55.098 --> 00:29:01.269
Are there too many bugs coming into the project unexpectedly, which will lower your planning accuracy?
00:29:01.878 --> 00:29:04.509
And it all relates together for on time delivery.
00:29:04.538 --> 00:29:08.808
So it's kind of like, if you improve productivity, you'll improve the experience.
00:29:08.808 --> 00:29:11.759
If you improve the experience, you'll improve on time delivery.
00:29:11.759 --> 00:29:14.654
If you improve on time delivery, you'll meet your business goals.
00:29:15.584 --> 00:29:24.784
And now my software engineering intelligence platform was worthwhile for me to roll out as a, as a buyer of this.
00:29:24.923 --> 00:29:26.233
CTO, a VP, yeah.
00:29:26.594 --> 00:29:29.153
What about this kind of next stage that we're already seeing?
00:29:29.153 --> 00:29:35.878
So you alluded to Automations to help with bot generated PRs and some of these bot automations we're seeing.
00:29:36.269 --> 00:29:42.078
But, uh, you know, GenAI is rapidly accelerating the amount of code that we're able to generate.
00:29:42.278 --> 00:29:46.108
Uh, it's making some really simple stuff even more simple.
00:29:46.318 --> 00:29:49.368
Uh, it's letting us, you know, code tests a lot faster.
00:29:49.378 --> 00:29:50.719
All these different use cases.
00:29:50.719 --> 00:29:59.564
How is, uh, An SEI platform like LinearB helping to improve the orchestration of Gen AI code within this offer to delivery life cycle.
00:29:59.923 --> 00:30:07.933
Yeah, I think like the way that I think about Gen AI, it's kind of like, what are we doing to help measure it first?
00:30:08.044 --> 00:30:13.023
So like with LinearB, we can measure the impact of your co pilot initiative for your developers.
00:30:13.584 --> 00:30:16.574
We can actually say, did it make us more productive?
00:30:16.574 --> 00:30:17.913
Did it cause more bugs?
00:30:17.913 --> 00:30:19.203
Like what was the outcome of it?
00:30:19.743 --> 00:30:21.134
I think that, I think that's one side of it.
00:30:22.159 --> 00:30:31.449
Then the second side of it is, okay, let's say that we know that Copilot and GenAI for developers is here to stay.
00:30:32.328 --> 00:30:39.288
Now we know that more code is going to be delivered faster, but where do you have the bottleneck now?
00:30:39.439 --> 00:30:42.759
It is usually in that review and deployment process.
00:30:43.959 --> 00:30:50.278
So making that smarter, make better decisions there, understand the risk of the change, put in policy and rules.
00:30:50.278 --> 00:30:51.538
That's what we do with GitStream.
00:30:51.554 --> 00:30:55.044
So it's kind of like help, help smooth it out.
00:30:55.943 --> 00:31:01.913
But then also it's kind of like, well, how are we using GenAI within our platform itself within SEI, right?
00:31:02.594 --> 00:31:08.884
So for example, right now we have a GenAI report for your sprint retrospective.
00:31:08.943 --> 00:31:09.564
Here's the thing.
00:31:09.564 --> 00:31:10.814
There's all this data.
00:31:10.834 --> 00:31:12.003
Think about a manager.
00:31:12.023 --> 00:31:13.604
Think about everyone working on the team.
00:31:13.854 --> 00:31:16.153
There's so much data coming into the platform.
00:31:16.153 --> 00:31:24.459
What GenAI is really good at is like distilling that data and making it human readable to say like, Here's what we saw with the bottlenecks.
00:31:24.479 --> 00:31:26.048
Here's what you should do next.
00:31:26.058 --> 00:31:28.159
Or here's how this sprint went for you.
00:31:28.709 --> 00:31:36.598
Or here's what we see happening within your people in terms of, you know, who is getting bogged down by toil and who's not.
00:31:36.618 --> 00:31:42.719
So it's kind of like distilling that information to something that's more digestible, human readable, and then actionable.
00:31:43.124 --> 00:31:44.943
So those are kind of like the phases that I see.
00:31:45.465 --> 00:31:55.145
Dan, what do you view as helping great SEI platforms stand out from the more copy and paste solutions that people are trying to pitch?
00:31:55.736 --> 00:31:57.326
Yeah, that's a good question.
00:31:57.506 --> 00:31:58.895
And I think it's pretty simple.
00:31:59.516 --> 00:32:05.875
I think a mediocre SEI platform is just giving you metrics.
00:32:06.365 --> 00:32:08.346
Maybe it's just giving you DORA metrics.
00:32:08.346 --> 00:32:08.486
Yeah.
00:32:08.875 --> 00:32:12.445
Maybe it's giving you some project delivery metrics, that type of stuff.
00:32:12.776 --> 00:32:17.306
But at the end of the day, it's giving you the data, but that data is just sitting on the screen.
00:32:18.316 --> 00:32:20.405
What is a great SEI platform?
00:32:20.405 --> 00:32:24.655
And this is why, you know, I like to say with LinearB, we're like SEI plus.
00:32:24.816 --> 00:32:25.915
We're more than just SEI.
00:32:26.865 --> 00:32:33.655
It has to do with the automations that you can deploy that actually help improvement.
00:32:33.715 --> 00:32:40.705
Now that could be improvement with efficiency, That could be improvement in developer experience, that could be improvement with project delivery.
00:32:41.195 --> 00:32:58.875
But a very, like, down to earth example would be, let's say that you log into your environment, and you see a piece of data, and that piece of data says, you have a bottleneck in your pull request review process.
00:32:59.415 --> 00:33:00.516
That's what the data says.
00:33:00.516 --> 00:33:10.701
And you know, a good SEI platform will show you a benchmark, And it will show you how you compare the industry average, and you can set a goal to improve, and all of that.
00:33:11.270 --> 00:33:22.671
But a great SEI platform says, the reason that you have this bottleneck, is we notice that most of the reviews are down to one person per team.
00:33:22.941 --> 00:33:25.401
Like a senior reviewer, and that person's the bottleneck.
00:33:26.260 --> 00:33:47.361
And by the way, we have an automation, a recommended automation, that you could deploy That does a round robin load balancing that will distribute the reviews to your, some of your junior managers, some of your mid, uh, some of your junior developers, some of your mid level developers, and that automation is going to unlock your bottleneck.
00:33:47.451 --> 00:33:54.861
That's a great SEI platform because it went from data to insight, to automated, automated solution.
00:33:55.201 --> 00:34:01.631
We're a mediocre one, or maybe one that's like only for free, or you're collecting it in a spreadsheet, or it's like a lower end one.
00:34:01.961 --> 00:34:04.621
We'll just say, Hey, your cycle time is bad.
00:34:05.074 --> 00:34:05.364
Yeah.
00:34:05.374 --> 00:34:13.514
They're, they're kind of thinking about it with this lens of a few years ago in the industry and not taking on the innovation approach of how do we actually help you orchestrate code?
00:34:13.523 --> 00:34:15.693
How do we actually help you deliver more predictability?
00:34:16.164 --> 00:34:17.963
They're just saying, Oh, we see a problem.
00:34:18.684 --> 00:34:19.123
That's right.
00:34:19.454 --> 00:34:23.393
Do you have any thoughts on kind of the next phase for software engineering intelligence?
00:34:23.393 --> 00:34:32.514
Like what's the new frontier now, as you have started to really automate some of the improvements, uh, get better at pulling in insights, you know, kind of adapt to gen AI.
00:34:32.514 --> 00:34:34.923
What, what's kind of the next horizon?
00:34:35.503 --> 00:34:38.864
I think it's the co pilot for engineering managers, man.
00:34:39.023 --> 00:34:40.213
That's what I think it is.
00:34:40.583 --> 00:34:51.313
And I think like almost every profession is going to have a co pilot for the Co pilot, we know for developers, co pilot for lawyers, co pilot for CFOs.
00:34:51.414 --> 00:34:56.094
I think co pilot for team leaders, engineering managers, directors.
00:34:56.094 --> 00:35:00.494
That middle layer that I talked about is so tough.
00:35:01.014 --> 00:35:04.884
There is so much data flowing at you.
00:35:04.884 --> 00:35:06.534
You're responsible for people.
00:35:06.594 --> 00:35:09.003
You're responsible for project delivery.
00:35:09.253 --> 00:35:11.454
You're responsible for high quality code.
00:35:11.759 --> 00:35:13.809
You're responsible for an effective workflow.
00:35:14.358 --> 00:35:24.048
But now imagine every morning you wake up and your co pilot is sending you information, probably over Slack, that just says, Hey, here's the three areas to look at today.
00:35:24.048 --> 00:35:26.818
I've already monitored what was going on.
00:35:27.059 --> 00:35:29.108
You might want to have a one on one with this person.
00:35:30.099 --> 00:35:38.465
You could improve your workflow by, uh, deploying this, code expert automation to find the right reviewer for some load balancing.
00:35:38.465 --> 00:35:38.516
Bye.
00:35:39.425 --> 00:35:43.996
And also over here, there's a few sensitive code changes that you might want to look at.
00:35:44.326 --> 00:35:46.905
I know you can't review everything, but you might want to look at this.
00:35:47.346 --> 00:35:50.646
Wow, now I'm like 20 percent more effective.
00:35:51.005 --> 00:35:52.485
That's where I see it going.
00:35:53.565 --> 00:35:54.775
And that's what we're working on.
00:35:55.146 --> 00:36:06.436
So LinearB aggregates qualitative and quantitative data for software engineering leaders to help them make better decisions and remove these workflow bottlenecks with automations and other opportunities.
00:36:06.806 --> 00:36:15.726
And this kind of co pilot system, it can simply continue to be enhanced with, you know, new automations, new data sources.
00:36:16.210 --> 00:36:19.851
Uh, new you know, AI'd Opportunities, that's kind of what he's saying.
00:36:19.851 --> 00:36:23.280
Connect to your calendar, see how much focus time is happening for your developers.
00:36:23.661 --> 00:36:27.460
Hey, you know, on, on Friday I have time to look at some insights.
00:36:27.471 --> 00:36:30.871
Imagine, you know, what, I mean, we have customers doing this now.
00:36:30.920 --> 00:36:40.731
An insight that comes in and says, Hey Conor, just notice like for your team, the amount of focus time that your developers got last week was down 15%.
00:36:41.181 --> 00:36:42.960
First of all, you're probably going to hear this Monday.
00:36:43.001 --> 00:36:44.311
Now you're already ahead of the game.
00:36:44.900 --> 00:36:45.161
Yeah.
00:36:45.201 --> 00:36:45.731
Oh, okay.
00:36:45.731 --> 00:36:47.731
Hey, now imagine coming in Monday morning.
00:36:47.731 --> 00:36:49.280
Hey, I know that we didn't get as much.
00:36:49.311 --> 00:36:50.871
We had too many meetings last week.
00:36:50.911 --> 00:36:51.710
I know that.
00:36:52.371 --> 00:36:58.561
Um, let's talk about which ones we need to remove so that everyone can focus on developing this week.
00:36:58.971 --> 00:37:00.481
That's a good manager.
00:37:01.300 --> 00:37:07.371
A bad manager is you find out two weeks later, but all your developers know they've been complaining.
00:37:07.400 --> 00:37:08.721
You see, see the difference?
00:37:09.161 --> 00:37:09.440
Yeah.
00:37:09.471 --> 00:37:13.811
That's where I think we can just like excel, accelerate, make you a better manager.
00:37:14.186 --> 00:37:16.856
That makes total sense.
00:37:17.326 --> 00:37:20.155
Do you have any other thoughts around SEI you want to share with the audience?
00:37:20.246 --> 00:37:24.436
This has been a great dive into it, but I wonder if there's other areas you want to dive into.
00:37:24.485 --> 00:37:29.155
First of all, like, I'm happy to say that the space is here.
00:37:29.255 --> 00:37:30.545
It is here to stay.
00:37:30.646 --> 00:37:38.485
I think you should look into it so that you're, you know, whether we say by 2027, 50%, 50 percent growth, something like that.
00:37:38.485 --> 00:37:39.456
Look into it now.
00:37:39.981 --> 00:37:41.260
Make sure you're on top of it.
00:37:41.701 --> 00:37:55.141
And then if you are on top of it, make sure that you're aligning it to initiatives and using automations for your people, like the co pilot thing that I talked about, the insights are coming to your people, and then make sure that you're automating your SDLC.
00:37:55.141 --> 00:37:56.291
You're doing those things.
00:37:56.291 --> 00:37:57.621
You're ahead of the game.
00:37:57.621 --> 00:37:57.675
You're doing it.
00:37:59.106 --> 00:38:00.615
That's what I'd love to see everybody do.
00:38:01.126 --> 00:38:01.416
Yeah.
00:38:01.416 --> 00:38:08.695
As Dan said, Gartner believes that by 2027, 50 percent of engineering teams will have adopted software engineering intelligence.
00:38:08.786 --> 00:38:22.996
So if you're one of those teams that is looking to evaluate SEI platforms or better understand the space, I know LinearB has created a free essential guide to SEI, uh, to help you explore those platforms and you can download it at LinearB.
00:38:22.996 --> 00:38:24.056
io slash resources.
00:38:24.126 --> 00:38:26.686
We'll also include a link, uh, in the show nights here.
00:38:27.141 --> 00:38:29.900
Um, Dan, thank you so much for this great conversation and thanks everyone for tuning in.
00:38:30.443 --> 00:38:31.092
Thanks, Conor.
