Demo Data from VLDB 2026 / reviewers in the wild / expert
B. Marr
dblp:21/1802
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2001
—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 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing › test generation
constraint-based test generation |
0.0 | 1 | 2001 | A New Way of Automating Statistical Testing Methods · ASE 2001 |
Software testing
statistical testing |
0.0 | 1 | 2001 | A New Way of Automating Statistical Testing Methods · ASE 2001 |
Software testing
structural testing |
0.0 | 1 | 2001 | A New Way of Automating Statistical Testing Methods · ASE 2001 |
Software testing
test generation |
0.0 | 1 | 2001 | A New Way of Automating Statistical Testing Methods · ASE 2001 |
Methods — techniques the papers use, named apart from their topics
randomized constraint solving · 0.0combinatorial structure generation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | A New Way of Automating Statistical Testing MethodsabstractWe propose a novel way of automating statistical structural testing of software, based on the combination of uniform generation of combinatorial structures, and of randomized constraint solving techniques. More precisely, we show how to draw test cases which balance the coverage of program structures according to structural testing criteria. The control flow graph is formalized as a combinatorial structure specification. This provides a way of uniformly drawing execution paths which have suitable properties. Once a path has been drawn, the predicate characterizing those inputs which lead to its execution is solved using a constraint solving library. The constraint solver is enriched with powerful heuristics in order to deal with resolution failures and random choice strategies. Sandrine-Dominique Gouraud, Alain Denise, Marie-Claude Gaudel, B. Marr |
ASE | 4 |