EDBT 2026 Demo / reviewers in the wild / expert
Karl-Heinz Prommer
dblp:64/10031
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2
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 · 61% Empirical software engineering · 30% Software maintenance and evolution · 9% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis › code quality analysis
dead code detection |
0.1 | 1 | 2012 | How much does unused code matter for maintenance? · ICSE 2012 |
Empirical software engineering › software analytics
deployed software analysis |
0.1 | 1 | 2012 | How much does unused code matter for maintenance? · ICSE 2012 |
Program analysis
dynamic analysis |
0.1 | 1 | 2012 | How much does unused code matter for maintenance? · ICSE 2012 |
Methods — techniques the papers use, named apart from their topics
static analysis · 0.1dynamic analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | How much does unused code matter for maintenance?abstractSoftware systems contain unnecessary code. Its maintenance causes unnecessary costs. We present tool-support that employs dynamic analysis of deployed software to detect unused code as an approximation of unnecessary code, and static analysis to reveal its changes during maintenance. We present a case study on maintenance of unused code in an industrial software system over the course of two years. It quantifies the amount of code that is unused, the amount of maintenance activity that went into it and makes the potential benefit of tool support explicit, which informs maintainers that are about to modify unused code. Sebastian Eder, Maximilian Junker, Elmar Jürgens, Benedikt Hauptmann, Rudolf Vaas, Karl-Heinz Prommer |
ICSE | 6 |
| 2011 | Feature Profiling for Evolving SystemsabstractTraditionally, most work in program comprehension focuses on understanding the inner workings of software systems. However, for many software maintenance tasks, not only a sound understanding of a system's implementation but also comprehensive and accurate information about the way users actually use a system's features is of crucial importance. Such information e.g. helps to determine the impact that a specific change has on the users of a system. In practice, however, this information is often not available. We propose an approach called feature profiling as a means to efficiently gather usage information to support maintenance tasks that affect the user interface of a software system. Furthermore, we present tool support for feature profiling and report on a case study in the insurance domain. In this study, we profiled the features of an application that is used by 150 users in 10 countries over a period of five months. Elmar Jürgens, Martin Feilkas, Markus Herrmannsdoerfer, Florian Deißenböck, Rudolf Vaas, Karl-Heinz Prommer |
ICPC | 6 |