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No Silver Bullets: Why Understanding Software Cycle Time is Messy, Not Magic

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.

Understanding and effectively mitigating code review anxiety - Empirical Software Engineering

Anxiety about giving and receiving code reviews has been documented as a common occurrence that leads to developers avoiding code reviews by procrastinating and limiting their cognitive engagement with them. This avoidance not only increases anxiety in the long term, but also prevents developers, their teams, and their organizations from accessing the technical and sociocognitive benefits of effective and efficient code reviews. However, software research has not yet empirically examined code review anxiety, and from this, tractable intervention targets and strategies for mitigating code review anxiety. In this study, we present an empirical framework for understanding the factors maintaining and exacerbating code review anxiety. Utilizing a randomized waitlist control trial, we also tested the effectiveness of a novel single-session cognitive-behavioral workshop intervention. Our results show evidence that positive impact can be obtained from a brief intervention and suggest code review anxiety can be successfully mitigated by targeting developers’ cost bias, anxiety self-efficacy, and self-compassion.

Incidents community-group

We would love to register your interest to be part of a small (4-5 person), one-time, group conversation about incidents in the context of software engineering and software engineering orgs. This conversation will help inform a new research project we are embarking on at the Developer Success Lab. The goal will largely be to develop ideas about this broad topic and help us find less visible areas of work involving incidents to bring into the light for the broader research and engineering community. You can find out more about our work here: https://dsl.pubpub.org/original-research We will contact you at the email you provide below. Thank you very much for taking the time to be here, and for your interest in contributing to this work.