@ARTICLE{Antoniol06-TSE-FeatureIdentification,
   AUTHOR       = {Giuliano Antoniol and Yann-Ga�l Gu�h�neuc},
   JOURNAL      = {Transactions on Software Engineering (TSE)},
   TITLE        = {Feature Identification: An Epidemiological Metaphor},
   YEAR         = {2006},
   MONTH        = {September},
   NOTE         = {15 pages.},
   NUMBER       = {9},
   PAGES        = {627--641},
   VOLUME       = {32},
   EDITOR       = {Tibor Gyim�thy and Vaclav Rajlich},
   KEYWORDS     = {Topic: <b>Requirements and features</b>, 
      Venue: <b>TSE</b>},
   PUBLISHER    = {IEEE CS Press},
   URL          = {http://www.ptidej.net/publications/documents/TSE06.doc.pdf},
   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.}
}

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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.","bibtex":"@ARTICLE{Antoniol06-TSE-FeatureIdentification,\r\n AUTHOR = {Giuliano Antoniol and Yann-Ga�l Gu�h�neuc},\r\n JOURNAL = {Transactions on Software Engineering (TSE)},\r\n TITLE = {Feature Identification: An Epidemiological Metaphor},\r\n YEAR = {2006},\r\n MONTH = {September},\r\n NOTE = {15 pages.},\r\n NUMBER = {9},\r\n PAGES = {627--641},\r\n VOLUME = {32},\r\n EDITOR = {Tibor Gyim�thy and Vaclav Rajlich},\r\n KEYWORDS = {Topic: <b>Requirements and features</b>, \r\n Venue: <b>TSE</b>},\r\n PUBLISHER = {IEEE CS Press},\r\n URL = {http://www.ptidej.net/publications/documents/TSE06.doc.pdf},\r\n ABSTRACT = {Feature identification is a technique to identify the \r\n source code constructs activated when exercising one of the features \r\n of a program. We propose new statistical analyses of static and \r\n dynamic data to accurately identify features in large multi-threaded \r\n object-oriented programs. We draw an inspiration from epidemiology to \r\n improve previous approaches to feature identification and develop an \r\n epidemiological metaphor. We build our metaphor on our previous \r\n approach to feature identification, in which we use processor \r\n emulation, knowledge-based filtering, probabilistic ranking, and \r\n meta-modelling. We carry out three case studies to assess the \r\n usefulness of our metaphor, using the ``save a bookmark\" feature of \r\n Web browsers as illustration. In the first case study, we compare our \r\n approach with three previous approaches (a naive approach, a concept \r\n analysis-based approach, and our previous probabilistic approach) in \r\n identifying the feature in \\ygg@product{Mozilla}, a large, real-life, \r\n multi-threaded object-oriented program. In the second case study, we \r\n compare the implementation of the feature in the \r\n \\ygg@product{Firefox} and \\ygg@product{Mozilla} Web browsers. In the \r\n third case study, we identify the same feature in two more Web \r\n browsers, Chimera (in \\C) and ICEBrowser (in Java), and another \r\n feature in \\ygg@product{JHotDraw} and \\ygg@product{Xfig}, to \r\n highlight the generalisability of our metaphor.}\r\n}\r\n\r\n","author_short":["Antoniol, G.","Gu�h�neuc, Y."],"editor_short":["Gyim�thy, T.","Rajlich, V."],"key":"Antoniol06-TSE-FeatureIdentification","id":"Antoniol06-TSE-FeatureIdentification","bibbaseid":"antoniol-guhneuc-featureidentificationanepidemiologicalmetaphor-2006","role":"author","urls":{"Paper":"http://www.ptidej.net/publications/documents/TSE06.doc.pdf"},"keyword":["Topic: <b>Requirements and features</b>","Venue: <b>TSE</b>"],"metadata":{"authorlinks":{"gu�h�neuc, y":"https://bibbase.org/show?bib=http%3A%2F%2Fwww.yann-gael.gueheneuc.net%2FWork%2FPublications%2FBiblio%2Fcomplete-bibliography.bib&msg=embed","guéhéneuc, 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