Feature Location using Probabilistic Ranking of Methods based on Execution Scenarios and Information Retrieval

Feature Location using Probabilistic Ranking of Methods based on Execution Scenarios and Information Retrieval. Poshyvanyk, D., Gu�h�neuc, Y., Marcus, A., Antoniol, G., & Rajlich, V. Transactions on Software Engineering (TSE), 33(6):420–432, IEEE CS Press, June, 2007. 14 pages.

Paper abstract bibtex

This paper recasts the problem of feature location in source code as a decision-making problem in the presence of uncertainty. The solution to the problem is formulated as a combination of expert opinions. The experts in this case are represented by two existing techniques for feature location: Scenario-based Probabilistic Ranking of events and an Information Retrieval-based technique that uses Latent Semantic Indexing. The combination of these two methods is empirically evaluated through several case studies. The case studies use the source code of the Mozilla web browser and the Eclipse integrated development environment. The results show that the combined technique significantly improves the effectiveness of feature location when compared to each of the techniques used independently.

@ARTICLE{Poshyvanyk07-TSE-Promesir,
   AUTHOR       = {Denys Poshyvanyk and Yann-Ga�l Gu�h�neuc and 
      Andrian Marcus and Giuliano Antoniol and V�clav Rajlich},
   JOURNAL      = {Transactions on Software Engineering (TSE)},
   TITLE        = {Feature Location using Probabilistic Ranking of Methods 
      based on Execution Scenarios and Information Retrieval},
   YEAR         = {2007},
   MONTH        = {June},
   NOTE         = {14 pages.},
   NUMBER       = {6},
   PAGES        = {420--432},
   VOLUME       = {33},
   EDITOR       = {Jeff Kramer},
   KEYWORDS     = {Topic: <b>Requirements and features</b>, 
      Venue: <b>TSE</b>},
   PUBLISHER    = {IEEE CS Press},
   URL          = {http://www.ptidej.net/publications/documents/TSE07.doc.pdf},
   ABSTRACT     = {This paper recasts the problem of feature location in 
      source code as a decision-making problem in the presence of 
      uncertainty. The solution to the problem is formulated as a 
      combination of expert opinions. The experts in this case are 
      represented by two existing techniques for feature location: 
      Scenario-based Probabilistic Ranking of events and an Information 
      Retrieval-based technique that uses Latent Semantic Indexing. The 
      combination of these two methods is empirically evaluated through 
      several case studies. The case studies use the source code of the 
      Mozilla web browser and the Eclipse integrated development 
      environment. The results show that the combined technique 
      significantly improves the effectiveness of feature location when 
      compared to each of the techniques used independently.}
}

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