Demo Data from EDBT 2026 / reviewers in the wild / expert
Edouard Delfosse
dblp:133/8208
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2013
—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 |
Requirements engineering and software design · 56% Empirical software engineering · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering
mining software repositories |
0.2 | 1 | 2013 | Feature model extraction from large collections of informal product descriptions · ESEC/SIGSOFT FSE 2013 |
Requirements engineering and software design
software product lines |
0.2 | 1 | 2013 | Feature model extraction from large collections of informal product descriptions · ESEC/SIGSOFT FSE 2013 |
Requirements engineering and software design › requirements analysis
domain analysis |
0.0 | 1 | 2013 | Feature model extraction from large collections of informal product descriptions · ESEC/SIGSOFT FSE 2013 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Feature model extraction from large collections of informal product descriptionsabstractFeature Models (FMs) are used extensively in software product line engineering to help generate and validate individual product configurations and to provide support for domain analysis. As FM construction can be tedious and time-consuming, researchers have previously developed techniques for extracting FMs from sets of formally specified individual configurations, or from software requirements specifications for families of existing products. However, such artifacts are often not available. In this paper we present a novel, automated approach for constructing FMs from publicly available product descriptions found in online product repositories and marketing websites such as SoftPedia and CNET. While each individual product description provides only a partial view of features in the domain, a large set of descriptions can provide fairly comprehensive coverage. Our approach utilizes hundreds of partial product descriptions to construct an FM and is described and evaluated against antivirus product descriptions mined from SoftPedia. Jean-Marc Davril, Edouard Delfosse, Negar Hariri, Mathieu Acher, Jane Cleland-Huang, Patrick Heymans |
ESEC/SIGSOFT FSE | 2 |