Demo Data from EDBT 2026 / reviewers in the wild / expert
John Lång
dblp:244/2106
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2019
0000-0002-9394-2464ORCID · reported
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 |
Program verification · 87% Software testing · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program verification
model checking |
0.4 | 1 | 2019 | Model checking a C++ software framework: a case study · ESEC/SIGSOFT FSE 2019 |
Program verification › model checking
software model checking |
0.4 | 1 | 2019 | Model checking a C++ software framework: a case study · ESEC/SIGSOFT FSE 2019 |
Software testing
unit testing |
0.1 | 1 | 2019 | Model checking a C++ software framework: a case study · ESEC/SIGSOFT FSE 2019 |
Methods — techniques the papers use, named apart from their topics
model checking · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Model checking a C++ software framework: a case studyabstractThis paper presents a case study on applying two model checkers, Spin and Divine, to verify key properties of a C++ software framework, known as ADAPRO, originally developed at CERN. Spin was used for verifying properties on the design level. Divine was used for verifying simple test applications that interacted with the implementation. Both model checkers were found to have their own respective sets of pros and cons, but the overall experience was positive. Because both model checkers were used in a complementary manner, they provided valuable new insights into the framework, which would arguably have been hard to gain by traditional testing and analysis tools only. Translating the C++ source code into the modeling language of the Spin model checker helped to find flaws in the original design. With Divine, defects were found in parts of the code base that had already been subject to hundreds of hours of unit tests, integration tests, and acceptance tests. Most importantly, model checking was found to be easy to integrate into the workflow of the software project and bring added value, not only as verification, but also validation methodology. Therefore, using model checking for developing library-level code seems realistic and worth the effort. John Lång, I. S. W. B. Prasetya |
ESEC/SIGSOFT FSE | 1 |