Demo Data from EDBT 2026 / reviewers in the wild / expert
Jeffery A. Solheim
dblp:85/2818
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1993
—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 |
Software testing · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing
integration testing |
0.0 | 1 | 1993 | An Empirical Study of Testing and Integration Strategies Using Artificial Software Systems · IEEE Trans. Software Eng. 1993 |
Software testing
unit testing |
0.0 | 1 | 1993 | An Empirical Study of Testing and Integration Strategies Using Artificial Software Systems · IEEE Trans. Software Eng. 1993 |
Methods — techniques the papers use, named apart from their topics
simulation · 0.0seeded defects · 0.0code generator · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1993 | An Empirical Study of Testing and Integration Strategies Using Artificial Software SystemsabstractThere has been much discussion about the merits of various testing and integration strategies. Top-down, bottom-up, big-bang, and sandwich integration strategies are advocated by various authors. Also, some authors insist that modules be unit tested, while others believe that unit testing diverts resources from more effective verification processes. This article addresses the ability of the aforementioned integration strategies to detect defects, and produce reliable systems. It also explores the efficacy of spot unit testing, and compares phased and incremental versions of top-down and bottom-up integration strategies. Relatively large artificial software systems were constructed using a code generator with ten basic module templates. These systems were seeded with known defects and tested using the above testing and integration strategies. A number of experiments were then conducted using a simulator whose validity was established by comparing results against these artificial systems. The defect detection ability and resulting system reliability were measured for each strategy. Results indicated that top-down integration strategies are generally most effective in terms of defect correction. Top-down and big-bang strategies produced the most reliable systems. Results favored neither those strategies that incorporate spot unit testing nor those that do not; also, results favored neither phased nor incremental strategies.> Jeffery A. Solheim, John H. Rowland |
IEEE Trans. Software Eng. | 1 |