Demo Data from EDBT 2026 / reviewers in the wild / expert
Jeffrey M. Barth
dblp:66/4362
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1977
—ORCID · none
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 analysis · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis
data flow analysis |
0.0 | 1 | 1977 | An Interprocedural Data Flow Analysis Algorithm · POPL 1977 |
Program analysis › static analysis
interprocedural analysis |
0.0 | 1 | 1977 | An Interprocedural Data Flow Analysis Algorithm · POPL 1977 |
Program analysis › data flow analysis
interprocedural dataflow analysis |
0.0 | 1 | 1977 | An Interprocedural Data Flow Analysis Algorithm · POPL 1977 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1977 | An Interprocedural Data Flow Analysis AlgorithmabstractA new interprocedural data flow analysis algorithm is presented and analyzed. The algorithm associates with each procedure in a program information about which variables may be modified, which may be used, and which are possibly preserved by a call on the procedure, and all of it subcalls. The algorithm is sufficiently powerful to be used on recursive programs and to deal with the sharing of variables which arises through reference parameters. The algorithm is unique in that it can compute all of this information in a single pass, not requiring a prepass to compute calling relationships or sharing patterns. A lower bound for the computational complexity of gathering interprocedural data flow information is derived and the algorithm is shown to be asymptotically optimal. The algorithm has been implemented and it is practical for use even on quite large programs. Jeffrey M. Barth |
POPL | 1 |