Feature Identification: A Novel Approach and a Case Study. Antoniol, G. & Gu�h�neuc, Y. In Gyim�thy, T. & Rajlich, V., editors, Proceedings of the 21<sup>st</sup> International Conference on Software Maintenance (ICSM), pages 357–366, September, 2005. IEEE CS Press. 10 pages. \awardBest paper.
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Paper abstract bibtex
Paper abstract bibtex Feature identification is a well-known technique to identify subsets of a program source code activated when exercising a functionality. Several approaches have been proposed to identify features. We present an approach to feature identification and comparison for large object-oriented multi-threaded programs using both static and dynamic data. We use processor emulation, knowledge filtering, and probabilistic ranking to overcome the difficulties of collecting dynamic data, i.e., imprecision and noise. We use model transformations to compare and to visualise identified features. We compare our approach with a naive approach and a concept analysis-based approach using a case study on a real-life large object-oriented multi-threaded program, \ygg@productMozilla, to show the advantages of our approach. We also use the case study to compare processor emulation with statistical profiling.
@INPROCEEDINGS{Antoniol05-ICSM-FeatureIdentification,
AUTHOR = {Giuliano Antoniol and Yann-Ga�l Gu�h�neuc},
BOOKTITLE = {Proceedings of the 21<sup>st</sup> International Conference on Software Maintenance (ICSM)},
TITLE = {Feature Identification: A Novel Approach and a Case
Study},
YEAR = {2005},
OPTADDRESS = {},
OPTCROSSREF = {},
EDITOR = {Tibor Gyim�thy and Vaclav Rajlich},
MONTH = {September},
NOTE = {10 pages. \awardBest paper.},
OPTNUMBER = {},
OPTORGANIZATION = {},
PAGES = {357--366},
PUBLISHER = {IEEE CS Press},
OPTSERIES = {},
OPTVOLUME = {},
KEYWORDS = {Topic: <b>Requirements and features</b>,
Venue: <c>ICSM</c>, Venue: <c>ICSME</c>},
URL = {http://www.ptidej.net/publications/documents/ICSM05.doc.pdf},
PDF = {http://www.ptidej.net/publications/documents/ICSM05.ppt.pdf},
ABSTRACT = {Feature identification is a well-known technique to
identify subsets of a program source code activated when exercising a
functionality. Several approaches have been proposed to identify
features. We present an approach to feature identification and
comparison for large object-oriented multi-threaded programs using
both static and dynamic data. We use processor emulation, knowledge
filtering, and probabilistic ranking to overcome the difficulties of
collecting dynamic data, i.e., imprecision and noise. We use model
transformations to compare and to visualise identified features. We
compare our approach with a naive approach and a concept
analysis-based approach using a case study on a real-life large
object-oriented multi-threaded program, \ygg@product{Mozilla}, to
show the advantages of our approach. We also use the case study to
compare processor emulation with statistical profiling.}
}Downloads: 0
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