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Abstract
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| 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. | |
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International
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Si |
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Congress
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International Symposium on Empirical Software Engineering and Measurement (ESEM) |
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960 |
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Place
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Baltimore, Mareyland (USA) |
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Reviewers
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Si |
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ISBN/ISSN
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978-0-7695-5056-5 |
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http://dx.doi.org/10.1109/ESEM.2013.60 |
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Start Date
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10/10/2013 |
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End Date
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11/10/2013 |
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From page
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233 |
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To page
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242 |
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Proceedings International Symposium on Empirical Software Engineering and Measurement (ESEM) |