Debunking 4 Big Software Development Myths

Let's jump in here. Welcome to the Jellyfish, um, debunking software development myths webinar. And we'd like to introduce ourselves. I'm Nick Arcolano. I'm the head of Jellyfish Research. Uh, we have on call here. Also, um, Mobo and Ben, you want to introduce yourselves? Mobo? Sure, I'm Mobo, also, um, in Jellyfish Research, a data scientist working on some fundamental questions, uh, with our data and some feature developments. Great. Ben?

Ben Kottrich, data scientist, Jellyfish Research. I've been here for about 9 months, and my work here focuses mostly on the work model, which is the piece of core Jellyfish tech that consumes the data exhaust from the various tools that software engineers use and makes inference about what people were working on when.

All right. So today we're gonna be talking about a bunch of research findings that folks from our group have investigated. To start, I just wanted to tell you a little bit about what Jellyfish Research is, who we are, what we do, but then we'll dive right into the results And we've got some interesting areas here to talk about. So some of the questions that we've looked at that we thought would be interesting are, are high priority issues completed faster? This is something we've investigated in the data that we've looked at here at Jellyfish. Do large pull requests receive more extensive reviews versus smaller pull requests? And then some results on story points. Do story points actually predict the amount of effort involved? And are companies and teams that use Story Points more efficient? Is that something we can see in the data? And then we'll wrap up and have some Q&A. And so briefly, I just wanted to talk about, you know, what is Jellyfish Research? You know, who are we? Hopefully you've heard of Jellyfish, the company and the product. You may not have heard of Jellyfish Research. Jellyfish Research is— we are the group at Jellyfish who— our goal is to empower the other departments and teams at Jellyfish, you know, to empower their ability to do innovation and research. And the way that we do that is through foundational research in key areas such as new data sources, product opportunities, fundamental advances in areas like statistics, data science, machine learning, through strategic research and prototyping. So, you know, jumping on and investigating new opportunities that are beyond or outside the scope of, you know, what our mainstream product dev teams are doing. We also spend a lot of time working on advanced algorithms, particularly things that use machine learning and production applications, so things like our work model that Ben mentioned. And we also support the other teams through analytics and insights. So, you know, providing the, the technology and expertise to help teams understand you know, what customers are using our product for and whether our solutions are solving the problems that we'd like them to for the people that use our product. And then briefly, I just wanted to talk a little bit about how our team is structured because I think it's interesting and kind of unique to us. And so Jellyfish Research, our group has, has two main parts. There's an embedded research group, uh, and that part of Jellyfish Research, those are basically So basically, analysts and data scientists who embed from our department into other teams and help do those things I was just describing to contribute to their team-specific mission. But then we also have a core research team, and that's a separate independent full-stack team. So that's a group of product researchers, data scientists, engineers. Ben and Mogo on the call here today with us are members of that group. And, you know, we sit apart from the other product dev teams and pursue long-term research based on our shared product vision across Jellyfish and hop on opportunistic things that lie outside the scope of other teams. And, you know, some of the results that we're going to talk about today came out of investigations, you know, within this mission. And There are shared goals for the entire department. You know, ultimately our goal is to help foster innovation across all of Jellyfish. And one of the ways that we do that is by helping develop the standards of foundational technology for things like analytics, machine learning infrastructure across the whole org. So, you know, we're really excited about, you know, the group, what we're able to do, the value that we try to provide both for Jellyfish, the company and the product, but also our customers.