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Cited by 322

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Mining version histories to guide software changes

Zimmermann, T., Weibgerber, P., Diehl, S., Zeller, A.·2004·Proceedings. 26th International Conference on Software Engineering
DOI 10.1109/icse.2004.1317478
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Abstract

We apply data mining to version histories in order to guide programmers along related changes: "Programmers who changed these functions also changed. . . ". Given a set of existing changes, such rules (a) suggest and predict likely further changes, (b) show up item coupling that is indetectable by program analysis, and (c) prevent errors due to incomplete changes. After an initial change, our ROSE prototype can correctly predict 26% of further files to be changed - and 15% of the precise functions or variables. The topmost three suggestions contain a correct location with a likelihood of 64%.

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Metadata

Authors
Zimmermann, T., Weibgerber, P., Diehl, S., & Zeller, A.
Venue
Proceedings. 26th International Conference on Software Engineering
Publisher
IEEE Comput. Soc
Published
2004-09-28
Pages
563-572
ISSN
0270-5257 (ISSN-L)
DOI
10.1109/icse.2004.1317478
OpenAlex
W4233240690
Cited by
322
References
11
FWCI
15.00
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Mining version histories to guide software changes. https://app.cochat.ai/papers/10.1109/icse.2004.1317478
The paper
Zimmermann, T., Weibgerber, P., Diehl, S., & Zeller, A. (2004). Mining version histories to guide software changes. Proceedings. 26th International Conference on Software Engineering. https://doi.org/10.1109/icse.2004.1317478

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.