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Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction
Abstract
A key issue in software evolution analysis is the identification of particular changes that occur across several versions of a program. We present change distilling, a tree differencing algorithm for fine-grained source code change extraction. For that, we have improved the existing algorithm of Chawathe et al. for extracting changes in hierarchically structured data. Our algorithm detects changes by finding a match between nodes of the compared two abstract syntax trees and a minimum edit script. We can identify change types between program versions according to our taxonomy of source code changes. We evaluated our change distilling algorithm with a benchmark we developed that consists of 1,064 manually classified changes in 219 revisions from three different open source projects. We achieved significant improvements in extracting types of source code changes: our algorithm approximates the minimum edit script by 45% better than the original change extraction approach by Chawathe et al. We are able to find all occurring changes and almost reach the minimum conforming edit script, i.e., we reach a mean absolute percentage error of 34%, compared to 79% reached by the original algorithm. The paper describes both the change distilling and the results of our evaluation.
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Metadata
- Authors
- Fluri, B., Wursch, M., PInzger, M., & Gall, H.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2007-11
- Volume
- 33
- Issue
- 11
- Pages
- 725-743
- ISSN
- 0098-5589 (ISSN-L)
- 1939-3520
- 2326-3881
- OpenAlex
- W2153150125
- Cited by
- 607
- References
- 0
- FWCI
- 55.90
- Type
- journal article
- Language
- en
- Open access
- closed
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- Fluri, B., Wursch, M., PInzger, M., & Gall, H. (2007). Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction. IEEE Transactions on Software Engineering. https://doi.org/10.1109/tse.2007.70731
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