EDBT 2026 Demo / reviewers in the wild / expert
Ben Greenberg
dblp:296/2122
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—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 · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering › developer studies
developer productivity study |
0.2 | 1 | 2023 | Improving Design Reviews at Google · ASE 2023 |
Methods — techniques the papers use, named apart from their topics
data analysis · 0.7automated workflow · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Improving Design Reviews at GoogleabstractDesign review is an important initial phase of the software development life-cycle where stakeholders gain and discuss early insights into the design's viability, discover potentially costly mistakes, and identify inconsistencies and inadequacies. For improved development velocity, it is important that design owners get their designs approved as Quickly as possible. In this paper, we discuss how engineering design reviews are typically conducted at Google, and propose a novel, structured, automated solution to improve design review velocity. Based on data collected on 141,652 approved documents authored by 41,030 users over four years, we show that our proposed solution decreases median time-to-approval by 25%, and provides further gains when used consistently. We also provide qualitative data to demonstrate our solution's success, discuss factors that impact design review latency, propose strategies to tackle them, and share lessons learned from the usage of our solution. Celal Ziftci, Ben Greenberg |
ASE | 2 |