Dev Interrupted

Labs: Everything You Need to Know About Software Engineering Intelligence (SEI)

• Episode 25

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Engineering leaders have long used value stream management and CI/CD tools to improve software delivery practices. However, an increasing demand for cost and efficiency is leading to the adoption of new technologies. Enterprises are quickly adopting tools that combine deeper levels of visibility into the SDLC with net-new workflow automations, leading to a better developer experience and increased output. 

This week's labs episode takes an in-depth look at Software Engineering Intelligence (SEI) Platforms and how engineering teams are using this new technology to gain a competitive advantage. LinearB’s COO and Co-founder Dan Lines along with co-host Conor Bronsdon cover the evolution of SEI, its core capabilities, and how these tools are being used to drive predictability, resource investment strategy and an improved developer experience. 

Join our journey into the data insights and workflow automations that are driving the next wave of continuous improvement. Gartner estimates that the adoption of SEI platforms will increase to 50% of engineering teams by 2027 – whether you're a VP, manager, or developer, find out why adopting an SEI Platform is crucial to your future success.

Episode Highlights:

  • 2:39 Digging into the data to find optimizations 
  • 4:02 What is Software Engineering Intelligence (SEI)? 
  • 9:08 What is profitable engineering and why should it be top of mind? 
  • 14:56 How can SEI help a VPE or CTO? 
  • 20:43 How does SEI relate to value stream management? 
  • 25:05 The role of automation in continuous improvement 
  • 29:36 How do SEI platforms help improve GenAI code orchestration? 
  • 31:45 What makes a great SEI platform? 
  • 34:19 What's next for SEI?

Show Notes:

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Dan Lines:

You're getting pressure to deliver projects on time. 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. Right. 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? 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.

Conor Bronsdon:

Gardener just released their market guide, showing that software engineering intelligence platforms, help engineering leaders significantly improve both team productivity and value delivery. Through Gardner's in-depth analysis on the critical features of sci platforms and how they can be used to drive engineering excellence. Linear B was named as a representative vendor. And therefore we're giving away a complimentary copy of Gartner's sci market guide. 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.

Dan Lines:

Hey, what's up everyone? We're back with another Labs episode of Dev Interrupted. I'm your host and linear BCOO and Co-founder Dan Lyons. Today I'll be joined by my co-host, Connor B. Great to see you as always, Connor.

Conor Bronsdon:

Likewise, Dan. Always great to be here and to hear about how badly your fantasy baseball team is doing.

Dan Lines:

I know we were just talking before, we're at five and six. We're battling for a playoff spot. You and I do fantasy football together. I think fantasy football is my number one. Fantasy baseball is more like, tie me over until football season starts, but definitely. Learning a lot about baseball.

Conor Bronsdon:

What I love is that you, not only in your career, but in your personal life, just, you love data. You're like, Hey, I really want to spend my free time figuring out data around a sport. Uh, you know, in my work life, you know, let's do data around engineering teams. How can I optimize these teams? They perform better. You know, it's almost like you have an obsession here or something.

Dan Lines:

That's actually funny. I think, honestly, probably a lot of our audience has seen Moneyball. Moneyball is like what turned me on to fantasy baseball.

Conor Bronsdon:

Mm-Hmm. Yeah. Checking the

Dan Lines:

data, looking at the box score.

Conor Bronsdon:

Where's the optimization we can find people aren't necessarily seeing so far.

Dan Lines:

Yeah. A little nerdy, but yeah, I like it. Um,

Conor Bronsdon:

well, and I think that drives into kind of the things that we talk about with dev, developer productivity and developer experience, like. What are the, the workflow challenges that we can find, or what are the things that people are underrating? You know, slugging percentage, maybe for an example for baseball, that people aren't rating as highly as they need to. Um, our strikeouts a big issue, or you know, our, our long PR is a big issue, like these kind of optimizations.

Dan Lines:

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. This happens all the time, both in business and sports. Like I'm watching a game. I, and, and sometimes I'm proven right and sometimes I'm proving wrong. Like I think a player is getting better. Actually, like the underlying data says that's not the case or vice versa. Like. Something on my, uh, in business is like, Oh, I think we might have a problem in this area of the workflow. Actually, the data says we're okay. It's somewhere else. So I think that's also cool to see like that. Hey, this is what I think's happening. What's the data show me that type of thing. So yeah, maybe that's where the fantasy, uh, is. Uh, Sports Addiction comes from.

Conor Bronsdon:

Well, and I know that's the topic for what you want to chat about today, right? Which is, you know, how are we understanding the problems and then solving them that are happening in software engineering?

Dan Lines:

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. I'm sure most people on the pod know what You What's up with Gartner? 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. 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. So it's like hot topic picking up. That's where we're going to dive in today. What is up with this SEI stuff?

Conor Bronsdon:

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?

Dan Lines:

Yeah, well, we probably should have said that first, right? 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. I would say the first incarnation of it, I would describe it something like this. Okay. SEI, it's this data driven visibility and insights into how engineering teams operate and improve their efficiency. That's maybe how I would talk about it like five years ago. And it's not that it's wrong, but I actually think the space, uh, has evolved. We've all evolved with it. And now I would say. 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. It's really about transforming these. I would call them more like, uh, like business value initiatives with data. 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.

Conor Bronsdon:

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. 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. 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?

Dan Lines:

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. Say that you want to do that. Okay, you understand the space is heating up. You got to get data driven. Now you want to start an initiative. What do you need to do? So maybe let me describe some of the things that make up SEI. I think first and foremost, it's around predictable or on time project delivery. I'll give like the, the real stories behind that. I guess that's the other thing I like about this SCI space now. Went from just like this, yeah, measure everything with data to like real life stuff. Real life stuff is, I'm a VP of engineering. The number one question that I get asked is, when is the project going to be done? When's the feature coming out? That's what I'm being asked by my CEO. That's what I'm being asked by the sales team. That's what I'm being asked by the product team. So the first core module of SEI is what I call on time project delivery. That's probably my favorite one. Now there's a bunch of other ones. I'll list them all out, but maybe, maybe that's the first one. The other ones are around profitable engineering, resource alignment. Are we investing into the right things? Where's the money going into our projects and people like that's super important. 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. To staff your development team. Where is that money going? And what's the outcome for our business? Right.

Conor Bronsdon:

Yeah. So, so you mentioned this phrase, profitable engineering, and I, I've started to hear that now in the industry lately. What does that mean to you and why should engineering leaders be thinking about engineering profitability?

Dan Lines:

First of all, I think it sounds really cool. Profitable engineering. It's catchy, right?

Conor Bronsdon:

Absolutely.

Dan Lines:

Let's like do the non catchy version. 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? And do a translation of that cost back to your business folks. Again, could be CEO, could be anyone on the executive team, could be your board. And justify that you should be investing in project A. 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. And then how can you translate to say, this is how I think it will help the business. That's how I think about profitable engineering and Conor, what I would say, it's like so important actually for software engineering intelligence. Cause again, if you're just looking at metrics of like cycle time, not saying cycle time's bad or something like that. 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? 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. And that's what I like about it.

Conor Bronsdon:

So why do you think these software engineering intelligence platforms are coming to the forefront now?

Dan Lines:

Yeah, no, that, that's an awesome question. I actually thought a lot about it over the last few weeks, cause that's what I do in my spare time. And I don't think that there's one reason, but there's a collection of reasons. 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. Like if we visualize the data of how engineering works, how product engineering works, I think it can help us. And the reason I think it all happened at the same time or similar time is because you saw that in all different areas. Like sales has data, marketing has data. They've had it for a long time. But even like, uh, HR has data now, People Analytics, Engagement Survey, so that, that's like one thing that happened. Then the second thing that happened is, our ability to collect this data is easier than it's ever been. 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. We can see all of your releases. We can see everything with CICB. We can see all the tooling that you're using in the cloud. Sneak. So it's like not only did everyone say, Hey, I think this data would be useful. We can actually get the data pretty easily. Now you can get LinearB up and running with a few clicks of a button. That wasn't possible a long time ago. All right. So I think that's the second reason. Then I think these two other ones are more like what's happening right now. Every company is a software company now. You have companies that sell, I won't say the company's names, but you have companies that sell burgers, cheeseburgers. They're software companies. You order your burgers on your mobile app. They're getting more mobile app orders than go up to the counter orders. They have thousands of developers working. You have companies that sell tractors. 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. How do you maintain agriculture using drones and all this like insane technology, soft farming, farming is like software now. 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. 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. Yeah, but to your point, everyone's doing it now. Software has eaten the world. So I think, I think that's the third reason. And then the fourth reason, I think this is the kicker. I do think the AI movement is helping a lot. 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. By bots. Wow. 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? 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? And I think if you put those four things together, that's why the space is now mandatory, let's say, or exploding.

Conor Bronsdon:

Do you think that kind of every level of an engineering team can benefit from the software engineering intelligence solutions?

Dan Lines:

Yeah, I do, but I think, I think that they do it in different ways. So the short answer is yes, but it's like different depending on where you work, right, in the org structure.

Conor Bronsdon:

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?

Dan Lines:

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. Like you're getting pressure to deliver projects on time. 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. Right. 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? 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. Now if I keep going, because I think you asked then about like the manager or the director?

Conor Bronsdon:

Yeah, the person who's maybe managing the platform engineering team or managing a team of devs.

Dan Lines:

I think it's one of the hardest jobs in engineering. We have a bunch of, I think, earlier episodes, like being an engineering team leader, being a group manager. I think that's the hardest job, this middle management layer. 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. And you have to be technical, but you also have to have the soft skills. There's a lot coming at you. And for example, what we're doing at LinearB, you can think about it as like a co pilot for engineering managers. Deliver these people insights so that they can be, you know, developers are always like complaining, I have a terrible manager. It's because 50 percent of the time they probably do have a terrible manager. It's really hard. 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? So I think like, uh, at the management level, it's like day to day decision making with data. It's supercharged that. Helping coach your devs too. Yeah. Oh yeah. Of course. Of course. Like I'm, I'm now responsible for coaching, uh, 15 devs or whatever it is, eight to 15 devs, maybe more. 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. How do I grow my career? Am I contributing to the team in the right way? Am I contributing to pull request reviews, or am I only coding? That's definitely an initiative that a lot of the VPs are pushing, and it's hard on managers.

Conor Bronsdon:

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. I'm trying to level up. I'm trying to move to a new role. Uh, let me get quantitative and qualitative data, and then automations. And other unblocking opportunities through the platform that can help me to improve as an individual or as a team leader. And that it really helps manage, let's say we acquired a new company. Uh, how can we now evaluate how they're integrating with, uh, the rest of our engineering or the rest of our product org? Um, I mean, there's a ton of examples of this. 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.

Dan Lines:

Well, just like a real, real life example, a real life, real life example, we're working with a company right now. And I think everyone listening, if you're not a developer, you can remember what it was like to be a developer. And actually, the career ladder is a little bit vague. Like, what's the difference between a junior developer and a mid level developer? Or a mid level developer and a senior developer? Or a senior developer and a principal developer? In a lot of organizations, it's not defined that well. 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. Now the company we're working with, they want to make it more fair. There's all types of developers, right? 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. Now they're saying, hey, with data, we just want to show the capabilities that it takes to be junior, mid, Senior and Principal. And for them, for example, one of the big things that they're pushing is how do you contribute to helping other teammates? 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? And they can see that with the data. 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. It picked up from like almost zero, you know, reviews that they're helping with or one per week to like two to three. Can we talk about that? I saw that out of you. That's awesome, man. So it's really like career progression.

Conor Bronsdon:

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. 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. 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. 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. How do you see this concept of software engineering intelligence and what platforms that provide SEI can deliver relating to value stream management?

Dan Lines:

So when I When we first founded LinearB like six, seven years ago, there was this concept of VSM, Value Stream Management. 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. Then I'm going to track it all the way around to renewals. And I'm going to therefore manage my value stream and we're going to get better. 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. It's an amazing idea in concept, but I haven't seen it actually implemented very well. 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. It fits in the middle of that. It's four product delivery teams. It's kind of like in the middle of that whole VSM end to end. And you can roll it out and you can get all the data for it and you can actually improve. So I don't know. 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. And that's the big difference.

Conor Bronsdon:

So maybe SEI platforms provide an opportunity to actually realize the value of ESM and say, okay, how do we implement this? How do we actually put this into practice?

Dan Lines:

I think so.

Conor Bronsdon:

You've mentioned a couple other key initiatives that are common themes or refrains on this show. Developer productivity and developer experience as things that SEI platforms can help teams solve or improve. How do you see SEI driving developer productivity?

Dan Lines:

Well, it's so great because those two things, productivity and experience, go hand in hand. That's what I love about it. Sometimes I think it's just like the way that you're viewing it. Being a developer, at the end of the day, you want to get your code out to prod. 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. And if I just simplify it, it's like, what's preventing me to do that? Where's the toil located where it's like more difficult for me to actually get my job done? 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? So therefore, it's kind of like measuring that experience for the end, for the engineer, what are they going through? And when you turn on an SEI platform for an organization, most of the time you will see a bottleneck in that workflow. 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. So most of the time it goes hand in hand for me.

Conor Bronsdon:

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. Turning insights into improvement by actually providing automation's built in platform. 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. Can you explain a bit more about how you view LinearB as going beyond the basics?

Dan Lines:

Yeah, I just, I think, again, it's the evolution of the space, like software engineering, intelligence, yeah, the intelligence side of it. Some of it is I have an insight and I'm delivering that insight to a manager so they can do their job better. 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. So for example, at LinearB, if you go into LinearB now, we have a full marketplace of automations. Some of those automations help new hires onboard more efficiently. Some of them ensure Data integrity or the definition of done of a PR. 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. So it's like how, how we work, right? Other times I mentioned, cause I just think it's the coolest one. Cause now we're finding all these bots are creating code. Some of these automations say, Hey, let's look at this Dependabot and say, how risky is this code change? And if it's really not risky at all, it's like a minor, minor change. Let's not ruin the developer experience, pull them away from coding. Let's not do that. In that situation, if all the tests pass, let's approve it. And yeah, if it's a major version bump, let's pull a developer. So it. You can kind of think of this automation marketplaces. Yes. Step one of SEI was data that shows where the bottlenecks are, but really where we've took the industry is. Automation to unlock those bottlenecks. And I think that's maybe, that's where I believe that the space is going. Delivery of intelligence into your SDLC and to your managers.

Conor Bronsdon:

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.

Dan Lines:

Yeah, I think the difference is, is like you need both. 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? So when you log into an SEI platform, you log into our platform, it shows you that. Here's where you focus your time. 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? Oh, go check out the automation library. Here's all these automation that other customers are using. Other people wrote, already solved your problem. Load balancing reviews, for example, finding an expert reviewer. Uh, creating, automatically creating a JIRA ticket when, there's a vulnerability found, there's people that have already done it. So it's like, Oh, okay. I can almost like, just like drag and drop and say like, this is going to help me. And I think that, that's the difference.

Conor Bronsdon:

And this relates back to that predictability element you mentioned earlier as well as dev experience. 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?

Dan Lines:

I think, I think it all, all relates together. Like, if you think predictability, we usually look at two metrics, like planning accuracy. Capacity, Accuracy. Now we've added in a forecasting. When will the, what day will the project be delivered on? We're using a Monte Carlo simulation to do so. But if you look at all of that stuff, it's like, what affects the delivery date? Well, the experience of the developer, are there bottlenecks in the, in the process? You can still think of some of your classic DORA metrics. Like, are they getting pulled away to production? Cause your change failure rate is too high. Are there too many bugs coming into the project unexpectedly, which will lower your planning accuracy? And it all relates together for on time delivery. So it's kind of like, if you improve productivity, you'll improve the experience. If you improve the experience, you'll improve on time delivery. If you improve on time delivery, you'll meet your business goals. And now my software engineering intelligence platform was worthwhile for me to roll out as a, as a buyer of this. CTO, a VP, yeah.

Conor Bronsdon:

What about this kind of next stage that we're already seeing? So you alluded to Automations to help with bot generated PRs and some of these bot automations we're seeing. But, uh, you know, GenAI is rapidly accelerating the amount of code that we're able to generate. Uh, it's making some really simple stuff even more simple. Uh, it's letting us, you know, code tests a lot faster. All these different use cases. How is, uh, An SEI platform like LinearB helping to improve the orchestration of Gen AI code within this offer to delivery life cycle.

Dan Lines:

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? So like with LinearB, we can measure the impact of your co pilot initiative for your developers. We can actually say, did it make us more productive? Did it cause more bugs? Like what was the outcome of it? I think that, I think that's one side of it. Then the second side of it is, okay, let's say that we know that Copilot and GenAI for developers is here to stay. Now we know that more code is going to be delivered faster, but where do you have the bottleneck now? It is usually in that review and deployment process. So making that smarter, make better decisions there, understand the risk of the change, put in policy and rules. That's what we do with GitStream. So it's kind of like help, help smooth it out. But then also it's kind of like, well, how are we using GenAI within our platform itself within SEI, right? So for example, right now we have a GenAI report for your sprint retrospective. Here's the thing. There's all this data. Think about a manager. Think about everyone working on the team. There's so much data coming into the platform. 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. Here's what you should do next. Or here's how this sprint went for you. 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. So it's kind of like distilling that information to something that's more digestible, human readable, and then actionable. So those are kind of like the phases that I see.

Conor Bronsdon:

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?

Dan Lines:

Yeah, that's a good question. And I think it's pretty simple. I think a mediocre SEI platform is just giving you metrics. Maybe it's just giving you DORA metrics. Yeah. Maybe it's giving you some project delivery metrics, that type of stuff. But at the end of the day, it's giving you the data, but that data is just sitting on the screen. What is a great SEI platform? And this is why, you know, I like to say with LinearB, we're like SEI plus. We're more than just SEI. It has to do with the automations that you can deploy that actually help improvement. Now that could be improvement with efficiency, That could be improvement in developer experience, that could be improvement with project delivery. 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. That's what the data says. 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. 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. Like a senior reviewer, and that person's the bottleneck. 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. That's a great SEI platform because it went from data to insight, to automated, automated solution. 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. We'll just say, Hey, your cycle time is bad.

Conor Bronsdon:

Yeah. 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? How do we actually help you deliver more predictability? They're just saying, Oh, we see a problem.

Dan Lines:

That's right.

Conor Bronsdon:

Do you have any thoughts on kind of the next phase for software engineering intelligence? 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. What, what's kind of the next horizon?

Dan Lines:

I think it's the co pilot for engineering managers, man. That's what I think it is. 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. I think co pilot for team leaders, engineering managers, directors. That middle layer that I talked about is so tough. There is so much data flowing at you. You're responsible for people. You're responsible for project delivery. You're responsible for high quality code. You're responsible for an effective workflow. 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. I've already monitored what was going on. You might want to have a one on one with this person. You could improve your workflow by, uh, deploying this, code expert automation to find the right reviewer for some load balancing. Bye. And also over here, there's a few sensitive code changes that you might want to look at. I know you can't review everything, but you might want to look at this. Wow, now I'm like 20 percent more effective. That's where I see it going. And that's what we're working on.

Conor Bronsdon:

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. And this kind of co pilot system, it can simply continue to be enhanced with, you know, new automations, new data sources. Uh, new you know, AI'd Opportunities, that's kind of what he's saying.

Dan Lines:

Connect to your calendar, see how much focus time is happening for your developers. Hey, you know, on, on Friday I have time to look at some insights. Imagine, you know, what, I mean, we have customers doing this now. 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%. First of all, you're probably going to hear this Monday. Now you're already ahead of the game. Yeah. Oh, okay. Hey, now imagine coming in Monday morning. Hey, I know that we didn't get as much. We had too many meetings last week. I know that. Um, let's talk about which ones we need to remove so that everyone can focus on developing this week. That's a good manager. A bad manager is you find out two weeks later, but all your developers know they've been complaining. You see, see the difference?

Conor Bronsdon:

Yeah.

Dan Lines:

That's where I think we can just like excel, accelerate, make you a better manager.

Conor Bronsdon:

That makes total sense. Do you have any other thoughts around SEI you want to share with the audience? This has been a great dive into it, but I wonder if there's other areas you want to dive into.

Dan Lines:

First of all, like, I'm happy to say that the space is here. It is here to stay. 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. Look into it now. Make sure you're on top of it. 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. You're doing those things. You're ahead of the game. You're doing it. That's what I'd love to see everybody do.

Conor Bronsdon:

Yeah. As Dan said, Gartner believes that by 2027, 50 percent of engineering teams will have adopted software engineering intelligence. 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. io slash resources. We'll also include a link, uh, in the show nights here. Um, Dan, thank you so much for this great conversation and thanks everyone for tuning in. Thanks, Conor.

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Dan Lines

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Conor Bronsdon

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