Feature Identification: An Epidemiological Metaphor. Antoniol, G. & Guéhéneuc, Y. Transactions on Software Engineering (TSE), 32(9):627–641, IEEE CS Press, September, 2006. 15 pages.Paper abstract bibtex 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 multi-threaded object-oriented programs. We draw an 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 meta-modelling. We carry out three case studies to assess the usefulness of our metaphor, using the ``save a bookmark" feature of Web browsers as 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 \ygg@productMozilla, a large, real-life, multi-threaded object-oriented program. In the second case study, we compare the implementation of the feature in the \ygg@productFirefox and \ygg@productMozilla 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 \ygg@productJHotDraw and \ygg@productXfig, to highlight the generalisability of our metaphor.
@ARTICLE{Antoniol06-TSE-FeatureIdentification,
author = {Giuliano Antoniol and Yann-Ga{\"e}l Gu{\'e}h{\'e}neuc},
title = {Feature Identification: {A}n Epidemiological Metaphor},
journal = {Transactions on Software Engineering ({TSE})},
year = {2006},
month = {September},
volume = {32},
number = {9},
pages = {627--641},
note = {15 pages.},
abstract = {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 multi-threaded object-oriented programs. We draw an 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 meta-modelling. We carry out three case studies to assess the usefulness of our metaphor, using the ``save a bookmark" feature of Web browsers as 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 \ygg@product{Mozilla}, a large, real-life, multi-threaded object-oriented program. In the second case study, we compare the implementation of the feature in the \ygg@product{Firefox} and \ygg@product{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 \ygg@product{JHotDraw} and \ygg@product{Xfig}, to highlight the generalisability of our metaphor.},
editor = {Tibor Gyim{\'o}thy and Vaclav Rajlich},
grant = {NSERC DG},
keywords = {Features and requirements ; TSE},
kind = {RIAS},
language = {english},
publisher = {IEEE CS Press},
url = {http://www.ptidej.net/publications/documents/TSE06.doc.pdf}
}
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