Head of Developer Experience | Developer Success Lab alumna | ultrarunner | writer | golfer
also on https://bsky.app/profile/kristen-fm.bsky.social
Background in language teaching pedagogy, Second Language Acquisition, and classroom-based research.
Passionate about integrating social and behavioral science into the tech world, particularly in enhancing how software teams and practitioners learn, work, and thrive.
Hot off the press, y'all - new research from the folks at the Developer Success Lab. https://arxiv.org/abs/2503.05040v2
I'm copying and pasting from an email I wrote to @flourn0 after reading a draft of the now available "No Silver Bullets: Why Understanding Software Cycle Time is Messy, Not Magic."
Because I don't think I can advocate for the quality of and need for this work any better than I did in that email:
Understanding factors that influence software development velocity is crucial for engineering teams and organizations, yet empirical evidence at scale remains limited. A more robust understanding of the dynamics of cycle time may help practitioners avoid pitfalls in relying on velocity measures while evaluating software work. We analyze cycle time, a widely-used metric measuring time from ticket creation to completion, using a dataset of over 55,000 observations across 216 organizations. Through Bayesian hierarchical modeling that appropriately separates individual and organizational variation, we examine how coding time, task scoping, and collaboration patterns affect cycle time while characterizing its substantial variability across contexts. We find precise but modest associations between cycle time and factors including coding days per week, number of merged pull requests, and degree of collaboration. However, these effects are set against considerable unexplained variation both between and within individuals. Our findings suggest that while common workplace factors do influence cycle time in expected directions, any single observation provides limited signal about typical performance. This work demonstrates methods for analyzing complex operational metrics at scale while highlighting potential pitfalls in using such measurements to drive decision-making. We conclude that improving software delivery velocity likely requires systems-level thinking rather than individual-focused interventions.
I know I'm a tad late to this conversation, but after reading through the original post and doing some research into this guy's background, it SEEMS clear that he's a serial entrepreneur trying to get people to use a product with this deedydas character...
So what I can't understanding is how Stanford is actually involved in this, outside of allowing Yegor to host his "research" sign-up on their .edu domain...
Hi, so has anyone figured out yet whether this Yegor guy is ACTUALLY doing research AT Standford?
As in, Stanford is employing him, or even just affiliating with him in any way to do EMPIRICAL research?
Because I can't find any trace that this is the case. The only thing I see is a guy who got an MBA from Stanford at some point this year separating "Stanford" and "Research" with a pipe char and making vague comments like "I'm at Stanford and I research software engineering productivity."
This is the most coherent book review I could muster at this time, shortly after finishing. If I could have given it 10 out of 5 stars, I would have. Please read it.
I'm excited to share that ⏰ @11:50 a.m. ET on April 25th, I'm moderating a session with Developer Success Lab research scientists @CSLee and @grimalkina titled "Science-Backed Strategies to Advance Your Tech Career."
In this session, Dr. Hicks and Dr. Lee will share their insights around topics like code review anxiety, learning culture, motivation, etc.
You can sign up here! We hope to see you there 😊 http://bit.ly/devsuccesslab-techskillsday
