Demo Data from SIGIR 2026 / reviewers in the wild / expert
Jack E. Matson
dblp:02/5970
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1994
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 first-author
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 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering › software size measurement
function point analysis |
0.0 | 1 | 1994 | Software Development Cost Estimation Using Function Points · IEEE Trans. Software Eng. 1994 |
Empirical software engineering
software effort estimation |
0.0 | 1 | 1994 | Software Development Cost Estimation Using Function Points · IEEE Trans. Software Eng. 1994 |
Methods — techniques the papers use, named apart from their topics
statistical regression analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1994 | Software Development Cost Estimation Using Function PointsabstractThis paper presents an assessment of several published statistical regression models that relate software development effort to software size measured in function points. The principal concern with published models has to do with the number of observations upon which the models were based and inattention to the assumptions inherent in regression analysis. The research describes appropriate statistical procedures in the context of a case study based on function point data for 104 software development projects and discusses limitations of the resulting model in estimating development effort. The paper also focuses on a problem with the current method for measuring function points that constrains the effective use of function points in regression models and suggests a modification to the approach that should enhance the accuracy of prediction models based on function points in the future.> Jack E. Matson, Bruce E. Barrett, Joseph M. Mellichamp |
IEEE Trans. Software Eng. | 1 |