Demo Data from VLDB 2026 / reviewers in the wild / expert
Frank P. A. Cohen
dblp:96/2165
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2002
—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 |
Software testing · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing
statistical testing |
0.0 | 1 | 2002 | Bayesian Graphical Models for Software Testing · IEEE Trans. Software Eng. 2002 |
Methods — techniques the papers use, named apart from their topics
probabilistic inference · 0.0bayesian graphical models · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2002 | Bayesian Graphical Models for Software TestingabstractThis paper describes a new approach to the problem of software testing. The approach is based on Bayesian graphical models and presents formal mechanisms for the logical structuring of the software testing problem, the probabilistic and statistical treatment of the uncertainties to be addressed, the test design and analysis process, and the incorporation and implication of test results. Once constructed, the models produced are dynamic representations of the software testing problem. They may be used to drive test design, answer what-if questions, and provide decision support to managers and testers. The models capture the knowledge of the software tester for further use. Experiences of the approach in case studies are briefly discussed. David A. Wooff, Michael Goldstein, Frank P. A. Cohen |
IEEE Trans. Software Eng. | 3 |