Demo Data from EDBT 2026 / reviewers in the wild / expert
Kaitlyn Cook
dblp:409/0582
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · unresolved
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 |
Requirements engineering and software design · 50% Empirical software engineering · 50% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Requirements engineering and software design › goal-oriented requirements engineering
goal modeling |
0.9 | 1 | 2025 | Exploring the Robustness of the Effect of EVO on Intention Valuation Through Replication · ICSE 2025 |
Empirical software engineering › reproducibility
replication study |
0.9 | 1 | 2025 | Exploring the Robustness of the Effect of EVO on Intention Valuation Through Replication · ICSE 2025 |
Methods — techniques the papers use, named apart from their topics
randomized experiment · 0.9pseudo-exact replication · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Exploring the Robustness of the Effect of EVO on Intention Valuation Through ReplicationabstractThe development of high-quality software depends on precise and comprehensive requirements that meet the objectives of stakeholders. Goal modeling techniques have been developed to fill this gap by capturing and analyzing stakeholders' needs and allowing them to make trade-off decisions; yet, goal modeling analysis is often difficult for stakeholders to interpret. Recent work found that when subjects are given minimal training on goal modeling and access to a color visualization, called EVO, they are able to use EVO to make goal modeling decisions faster without compromising quality. In this paper, we evaluate the robustness of the empirical evidence for EVO and question the underlying color choices made by the initial designers of EVO. We conduct a pseudo-exact replication$(n=60)$of the original EVO study, varying the experimental site and the study population. Even in our heterogeneous sample with less a priori familiarity with requirements and goal modeling, we find that individuals using EVO answered the goal-modeling questions significantly faster than those using the control, expanding the external validity of the original results. However, we find some evidence that the chosen color scheme is not intuitive and make recommendations for the goal modeling community. Yesugen Baatartogtokh, Kaitlyn Cook, Alicia M. Grubb |
ICSE | 2 |