Maggie Hodges
Maggie is a Senior User Experience Researcher on the Engineering Productivity Research team within the Developer Intelligence organization. The team takes a data-driven approach to business decisions by using a combination of qualitative and quantitative methods to triangulate on measuring productivity. Maggie received her Master's of Public Health from University of California, Berkeley.
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Measuring Developer Experience with a Longitudinal Survey
Jessica Lin
Jill Dicker
IEEE Software (2024)
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At Google, we’ve been running a quarterly large-scale survey with developers since 2018. In this article, we will discuss how we run EngSat, some of our key learnings over the past 6 years, and how we’ve evolved our approach to meet new needs and challenges.
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This is the seventh installment of the Developer Productivity for Humans column. This installment focuses on software quality: what it means, how developers see it, how we break it down into 4 types of quality, and the impact these have on each other.
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Developer Productivity for Humans, Part 5: Onboarding and Ramp-Up
Lanting He
Demei Shen
Nan Zhang
IEEE Software, 40 (2023), pp. 13-19
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In this installment of our column, we’ll describe some recent research on onboarding software developers, including some of the work that we’ve done with colleagues at Google to understand and measure developer onboarding and ramp-up at Google.
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Systemic Gender Inequities in Who Reviews Code
Emerson Murphy-Hill
Jill Dicker
Amber Horvath
Laurie R. Weingart
Nina Chen
Computer Supported Cooperative Work (2023) (to appear)
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Code review is an essential task for modern software engineers, where the author of a code change assigns other engineers the task of providing feedback on the author’s code. In this paper, we investigate the task of code review through the lens of equity, the proposition that engineers should share reviewing responsibilities fairly. Through this lens, we quantitatively examine gender inequities in code review load at Google. We found that, on average, women perform about 25% fewer reviews than men, an inequity with multiple systemic antecedents, including authors’ tendency to choose men as reviewers, a recommender system’s amplification of human biases, and gender differences in how reviewer credentials are assigned and earned. Although substantial work remains to close the review load gap, we show how one small change has begun to do so.
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Engineering Impacts of Anonymous Author Code Review: A Field Experiment
Emerson Rex Murphy-Hill
Jill Dicker
Lan Cheng
Liz Kammer
Ben Holtz
Andrea Marie Knight Dolan
Transactions on Software Engineering (2021)
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Code review is a powerful technique to ensure high quality software and spread knowledge of best coding practices between engineers. Unfortunately, code reviewers may have biases about authors of the code they are reviewing, which can lead to inequitable experiences and outcomes. In this paper, we describe a field experiment with anonymous author code review, where we withheld author identity information during 5217 code reviews from 300 professional software engineers at one company. Our results suggest that during anonymous author code review, reviewers can frequently guess authors’ identities; that focus is reduced on reviewer-author power dynamics; and that the practice poses a barrier to offline, high-bandwidth conversations. Based on our findings, we recommend that those who choose to implement anonymous author code review should reveal the time zone of the author by default, have a break-the-glass option for revealing author identity, and reveal author identity directly after the review.
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Predicting Developers’ Negative Feelings about Code Review
Emerson Murphy-Hill
Elizabeth Kammer
International Conference on Software Engineering (2020)
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During code review, developers critically examine each others’ code to improve its quality, share knowledge, and ensure conformance to coding standards. In the process, developers may have negative interpersonal interactions with their peers, which can lead to frustration and stress; these negative interactions may ultimately result in developers abandoning projects. In this mixed-methods study at one company, we surveyed 1,317 developers to characterize the negative experiences and cross-referenced the results with objective data from code review logs to predict these experiences. Our results suggest that such negative experiences, which we call “pushback”, are relatively rare in practice, but have negative repercussions when they occur. Our metrics can predict feelings of pushback with high recall but low precision, making them potentially appropriate for highlighting interactions that may benefit from a self-intervention.
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Enabling the Study of Software Development Behavior with Cross-Tool Logs
Ben Holtz
Edward K. Smith
Andrea Marie Knight Dolan
Elizabeth Kammer
Jillian Dicker
Caitlin Harrison Sadowski
Lan Cheng
Emerson Murphy-Hill
IEEE Software, Special Issue on Behavioral Science of Software Engineering (2020)
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Understanding developers’ day-to-day behavior can help answer important research questions, but capturing that behavior at scale can be challenging, particularly when developers use many tools in concert to accomplish their tasks. In this paper, we describe our experience creating a system that integrates log data from dozens of development tools at Google, including tools that developers use to email, schedule meetings, ask and answer technical questions, find code, build and test, and review code. The contribution of this article is a technical description of the system, a validation of it, and a demonstration of its usefulness.
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