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

Kaitlyn Cook

dblp:409/0582 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Requirements engineering and software design › goal-oriented requirements engineering
goal modeling
0.912025
Exploring the Robustness of the Effect of EVO on Intention Valuation Through Replication · ICSE 2025
Empirical software engineering › reproducibility
replication study
0.912025
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
YearPublicationVenuePosition
2025 Exploring the Robustness of the Effect of EVO on Intention Valuation Through Replication
abstract
The 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
ICSE2