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Feature Identification: An Epidemiological Metaphor
September 2006 (vol. 32 no. 9)
pp. 627-641
ASCII Text
x 
 
Giuliano Antoniol, Yann-Ga? Gu?h?neuc, "Feature Identification: An Epidemiological Metaphor," IEEE Transactions on Software Engineering, vol. 32, no. 9, pp. 627-641, September, 2006.
 
 
BibTex
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@article{ 10.1109/TSE.2006.88,
author = {Giuliano Antoniol and Yann-Ga? Gu?h?neuc},
title = {Feature Identification: An Epidemiological Metaphor},
journal ={IEEE Transactions on Software Engineering},
volume = {32},
number = {9},
issn = {0098-5589},
year = {2006},
pages = {627-641},
doi = {http://doi.ieeecomputersociety.org/10.1109/TSE.2006.88},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Software Engineering
TI - Feature Identification: An Epidemiological Metaphor
IS - 9
SN - 0098-5589
SP627
EP641
EPD - 627-641
A1 - Giuliano Antoniol,
A1 - Yann-Ga? Gu?h?neuc,
PY - 2006
KW - Program understanding
KW - dynamic analysis
KW - static analysis
KW - feature identification
KW - epidemiology
KW - Firefox and Mozilla Web browsers.
VL - 32
JA - IEEE Transactions on Software Engineering
ER -
 
Feature identification is a technique to identify the source code constructs activated when exercising one of the features of a program. We propose new statistical analyses of static and dynamic data to accurately identify features in large multithreaded object-oriented programs. We draw inspiration from epidemiology to improve previous approaches to feature identification and develop an epidemiological metaphor. We build our metaphor on our previous approach to feature identification, in which we use processor emulation, knowledge-based filtering, probabilistic ranking, and metamodeling. We carry out three case studies to assess the usefulness of our metaphor, using the "save a bookmark” feature of Web browsers as an illustration. In the first case study, we compare our approach with three previous approaches (a naive approach, a concept analysis-based approach, and our previous probabilistic approach) in identifying the feature in Mozilla, a large, real-life, multithreaded object-oriented program. In the second case study, we compare the implementation of the feature in the Firefox and Mozilla Web browsers. In the third case study, we identify the same feature in two more Web browsers, Chimera (in C) and ICEBrowser (in Java), and another feature in JHotDraw and Xfig, to highlight the generalizability of our metaphor.

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Index Terms:
Program understanding, dynamic analysis, static analysis, feature identification, epidemiology, Firefox and Mozilla Web browsers.
Citation:
Giuliano Antoniol, Yann-Ga? Gu?h?neuc, "Feature Identification: An Epidemiological Metaphor," IEEE Transactions on Software Engineering, vol. 32, no. 9, pp. 627-641, Sept. 2006, doi:10.1109/TSE.2006.88
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