[Zurück]

S. Biffl, E. Serral Asensio, D. Winkler, O. Dieste, N. Juristo, N. Condori-Fernández:
"Replication Data Management: Needs and Solutions - An initial evaluation of conceptual approaches for integrating heterogeneous replication study data";
Vortrag: 7th International Symposium on Empirical Software Engineering and Measurement, Baltimore, Maryland, USA; 10.10.2013 - 11.10.2013; in: "ACM / IEEE International Symposium on Empirical Software Engineering and Measurement, 2013", IEEE, (2013), ISBN: 978-0-7695-5056-5; S. 233 - 242.

[Context] Replication Data Management (RDM)
aims at enabling the use of data collections from several iterations
of an experiment. However, there are several major challenges
to RDM from integrating data models and data from empirical
study infrastructures that were not designed to cooperate,
e.g., data model variation of local data sources. [Objective] In this
paper we analyze RDM needs and evaluate conceptual RDM
approaches to support replication researchers. [Method] We
adapted the ATAM evaluation process to (a) analyze RDM use
cases and needs of empirical replication study research groups
and (b) compare three conceptual approaches to address these
RDM needs: central data repositories with a fixed data model,
heterogeneous local repositories, and an empirical ecosystem.
[Results] While the central and local approaches have major
issues that are hard to resolve in practice, the empirical ecosystem
allows bridging current gaps in RDM from heterogeneous
data sources. [Conclusions] The empirical ecosystem approach
should be explored in diverse empirical environments.

Infrastructure for conducting empirical studies, replication of empirical studies, software knowledge management