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Cited by 214

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Preprocessing CVS data for fine-grained analysis

Zimmermann, T.·2004·"International Workshop on Mining Software Repositories (MSR 2004)" W17S Workshop - 26th International Conference on Software Engineering
DOI 10.1049/ic:20040466
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Abstract

All analyses of version archives have one phase in common: the preprocessing of data. Preprocessing has a direct impact on the quality of the results returned by an analysis. In this paper we discuss four essential preprocessing tasks necessary for a fine-grained analysis of CVS archives: (a) data extraction, (b) transaction recovery, (c) mapping of changes to fine-grained entities, and (d) data cleaning. We formalize the concept of sliding time windows and show how commit mails can relate revisions to transactions. We also present two approaches that map changes to the affected building blocks of a file, e.g. junctions or sections.

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Metadata

Authors
Zimmermann, T.
Venue
"International Workshop on Mining Software Repositories (MSR 2004)" W17S Workshop - 26th International Conference on Software Engineering
Publisher
IEE
Published
2004
Volume
2004
Pages
2-6
DOI
10.1049/ic:20040466
OpenAlex
W2025674334
Cited by
214
References
0
FWCI
17.70
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Preprocessing CVS data for fine-grained analysis. https://app.cochat.ai/papers/10.1049/ic:20040466
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Zimmermann, T. (2004). Preprocessing CVS data for fine-grained analysis. "International Workshop on Mining Software Repositories (MSR 2004)" W17S Workshop - 26th International Conference on Software Engineering. https://doi.org/10.1049/ic%3A20040466

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.