Demo Data from EDBT 2026 / reviewers in the wild / expert
Anne von Vietinghoff
dblp:256/6146
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing
search-based software testing |
0.4 | 1 | 2019 | Experience Paper: Search-Based Testing in Automated Driving Control Applications · ASE 2019 |
Software testing
simulation-based testing |
0.4 | 1 | 2019 | Experience Paper: Search-Based Testing in Automated Driving Control Applications · ASE 2019 |
Software testing
test generation |
0.4 | 1 | 2019 | Experience Paper: Search-Based Testing in Automated Driving Control Applications · ASE 2019 |
Methods — techniques the papers use, named apart from their topics
optimization · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Experience Paper: Search-Based Testing in Automated Driving Control ApplicationsabstractAutomated test generation and evaluation in simulation environments is a key technology for verification of automated driving (AD) applications. Search-based testing (SBT) is an approach for automated test generation that leverages optimization to efficiently generate interesting concrete tests from abstract test descriptions. In this experience paper, we report on our observations after successfully applying SBT to AD control applications in several use cases with different characteristics. Based on our experiences, we derive a number of lessons learned that we consider important for the adoption of SBT methods and tools in industrial settings. The key lesson is that SBT finds relevant errors and provides valuable feedback to the developers, but requires tool support for writing specifications. Christoph Gladisch, Thomas Heinz 0001, Christian Heinzemann, Jens Oehlerking, Anne von Vietinghoff, Tim Pfitzer |
ASE | 5 |