Demo Data from EDBT 2026 / reviewers in the wild / expert
Sunder Kekre
dblp:83/2544
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1992
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1
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% Requirements engineering and software design · 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
COCOMO |
0.0 | 1 | 1992 | Software Effort Models for Early Estimation of Process Control Applications · IEEE Trans. Software Eng. 1992 |
Empirical software engineering
software effort estimation |
0.0 | 1 | 1992 | Software Effort Models for Early Estimation of Process Control Applications · IEEE Trans. Software Eng. 1992 |
Methods — techniques the papers use, named apart from their topics
regression modeling · 0.0function points · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1992 | Software Effort Models for Early Estimation of Process Control ApplicationsabstractModels are developed to estimate lines of code and function counts directly from user application features of process control systems early in the software development lifecycle. Since the application features are known with reasonable degree of confidence during early stages of development, it is possible to extend the use of the constructive cost model (COCOMO) and function-points-based approach for early software cost estimation. Alternative feature-based models that estimate size and effort using application features and productivity factors are developed. The feature-based models have been shown to estimate software effort with the least error.> Tridas Mukhopadhyay, Sunder Kekre |
IEEE Trans. Software Eng. | 2 |