Name
Abstract | ||
|---|---|---|
Developing new ideas and algorithms or comparing new findings in the field of requirements engineering and management implies a dataset to work with. Collecting the required data is time consuming, tedious, and may involve unforeseen difficulties. The need for datasets often forces re-searchers to collect data themselves in order to evaluate their findings. However, comparing results with other publications is especially difficult on proprietary datasets. A big obstacle is the reproduction of a previously used dataset, which may include subtle preprocessing steps not explicitly mentioned by the original authors. Providing a predefined dataset avoids these problems. It establishes a common baseline and enables direct comparison for benchmarking. This paper provides a well defined dataset consisting of seven open source software projects. It contains a large number of typed development artifacts and links between them. Enriched with additional metadata, such as time stamps, versions, and component information, the dataset allows answering a broad range of research questions. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/RE.2017.18 | 2017 IEEE 25th International Requirements Engineering Conference (RE) |
Keywords | DocType | ISSN |
mining software repositories,data collection,data mining,requirements analysis,traceability | Conference | 2332-6441 |
ISBN | Citations | PageRank |
978-1-5386-3192-8 | 2 | 0.40 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Rath | 1 | 17 | 3.37 |
| Patrick Rempel | 2 | 91 | 6.53 |
| Patrick Mäder | 3 | 492 | 36.96 |