Demo Data from EDBT 2026 / reviewers in the wild / expert
Scott Drylie
dblp:369/6517
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0001-2106-5765ORCID · reported
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 · 87% Software maintenance and evolution · 13% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering
software effort estimation |
0.8 | 1 | 2024 | An Assessment of Rules of Thumb for Software Phase Management, and the Relationship Between Phase Effort and Schedule Success · IEEE Trans. Software Eng. 2024 |
Empirical software engineering
software project management |
0.8 | 1 | 2024 | An Assessment of Rules of Thumb for Software Phase Management, and the Relationship Between Phase Effort and Schedule Success · IEEE Trans. Software Eng. 2024 |
Methods — techniques the papers use, named apart from their topics
least squares models · 0.8hotelling's t² test · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | An Assessment of Rules of Thumb for Software Phase Management, and the Relationship Between Phase Effort and Schedule SuccessabstractIn the planning of a software development project, managers must estimate the amount of effort needed for distinct phases of activity. A number of rules of thumb exist in the literature to help the program manager in this task. However, very little work has been done to validate these rules of thumb. Applying least square models and Hotelling's$T^{2}$test, we evaluate these rules of thumb against a large database of Department of Defense projects. We determine that variability limits the simple application of any such rule. However, there are some worthy of closer attention, and we recommend adjustments for improved application. We also determine that projects which give extra attention to early phases experience less schedule growth. These findings were robust across developmental process type, military service, and project size. Daniel Long, Scott Drylie, Jonathan D. Ritschel, Clay Koschnick |
IEEE Trans. Software Eng. | 2 |