Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Mark Canning

dblp:277/8891 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Empirical software engineering · 50% Software maintenance and evolution · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Empirical software engineering › developer studies › developer behavior
developer productivity
0.612022
What improves developer productivity at google? code quality · ESEC/SIGSOFT FSE 2022
Software maintenance and evolution
technical debt
0.612022
What improves developer productivity at google? code quality · ESEC/SIGSOFT FSE 2022

Methods — techniques the papers use, named apart from their topics

panel data analysis · 0.6lagged panel analysis · 0.6
YearPublicationVenuePosition
2022 What improves developer productivity at google? code quality
abstract
Understanding what affects software developer productivity can help organizations choose wise investments in their technical and social environment. But the research literature either focuses on what correlates with developer productivity in ecologically valid settings or focuses on what causes developer productivity in highly constrained settings. In this paper, we bridge the gap by studying software developers at Google through two analyses. In the first analysis, we use panel data with 39 productivity factors, finding that code quality, technical debt, infrastructure tools and support, team communication, goals and priorities, and organizational change and process are all causally linked to self-reported developer productivity. In the second analysis, we use a lagged panel analysis to strengthen our causal claims. We find that increases in perceived code quality tend to be followed by increased perceived developer productivity, but not vice versa, providing the strongest evidence to date that code quality affects individual developer productivity.
Emerson R. Murphy-Hill, Mark Canning, Ciera Jaspan, Collin Green, Andrea Knight, Elizabeth Kammer 0001
ESEC/SIGSOFT FSE3