Demo Data from SIGIR 2026 / reviewers in the wild / expert
Mark Canning
dblp:277/8891
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering › developer studies › developer behavior
developer productivity |
0.6 | 1 | 2022 | What improves developer productivity at google? code quality · ESEC/SIGSOFT FSE 2022 |
Software maintenance and evolution
technical debt |
0.6 | 1 | 2022 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | What improves developer productivity at google? code qualityabstractUnderstanding 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 FSE | 3 |