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Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction
November 2007 (vol. 33 no. 11)
pp. 725-743
ASCII Text
x 
 
Beat Fluri, Michael Wuersch, Martin PInzger, Harald Gall, "Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction," IEEE Transactions on Software Engineering, vol. 33, no. 11, pp. 725-743, November, 2007.
 
 
BibTex
x
 
@article{ 10.1109/TSE.2007.70731,
author = {Beat Fluri and Michael Wuersch and Martin PInzger and Harald Gall},
title = {Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction},
journal ={IEEE Transactions on Software Engineering},
volume = {33},
number = {11},
issn = {0098-5589},
year = {2007},
pages = {725-743},
doi = {http://doi.ieeecomputersociety.org/10.1109/TSE.2007.70731},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Software Engineering
TI - Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction
IS - 11
SN - 0098-5589
SP725
EP743
EPD - 725-743
A1 - Beat Fluri,
A1 - Michael Wuersch,
A1 - Martin PInzger,
A1 - Harald Gall,
PY - 2007
KW - Source code change extraction
KW - tree differencing algorithms
KW - software repositories
KW - software evolution analysis
VL - 33
JA - IEEE Transactions on Software Engineering
ER -
 
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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Index Terms:
Source code change extraction, tree differencing algorithms, software repositories, software evolution analysis
Citation:
Beat Fluri, Michael Wuersch, Martin PInzger, Harald Gall, "Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction," IEEE Transactions on Software Engineering, vol. 33, no. 11, pp. 725-743, Nov. 2007, doi:10.1109/TSE.2007.70731
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