@INPROCEEDINGS{Oliveto10-CSMR-BSplineSmellDetection,
   AUTHOR       = {Rocco Oliveto and Foutse Khomh and Giuliano Antoniol and 
      Yann-Ga�l Gu�h�neuc},
   BOOKTITLE    = {Proceedings of the 14<sup>th</sup> European Conference on Software Maintenance and Reengineering (CSMR)},
   TITLE        = {Numerical Signatures of Antipatterns: An Approach based 
      on B-Splines},
   YEAR         = {2010},
   OPTADDRESS   = {},
   OPTCROSSREF  = {},
   EDITOR       = {Rudolf Ferenc and Juan Carlos Due�as},
   MONTH        = {March},
   NOTE         = {5 pages. Short paper.},
   OPTNUMBER    = {},
   OPTORGANIZATION = {},
   PAGES        = {248--251},
   PUBLISHER    = {IEEE CS Press},
   OPTSERIES    = {},
   OPTVOLUME    = {},
   KEYWORDS     = {Topic: <b>Code and design smells</b>, 
      Venue: <c>CSMR</c>},
   URL          = {http://www.ptidej.net/publications/documents/CSMR10d.doc.pdf},
   PDF          = {http://www.ptidej.net/publications/documents/CSMR10d.ppt.pdf},
   ABSTRACT     = {Antipatterns are poor object-oriented solutions to 
      recurring design problems. The identification of occurrences of 
      antipatterns in systems has received recently some attention but 
      current approaches have two main limitations: either (1) they 
      classify classes strictly as being or not antipatterns, and thus 
      cannot report accurate information for borderline classes, or (2) 
      they return the probabilities of classes to be antipatterns but they 
      require an expensive tuning by experts to have acceptable accuracy. 
      To mitigate such limitations, we introduce a new identification 
      approach, ABS (Antipattern identification using B-Splines), based on 
      a numerical analysis technique. The results of a preliminary study 
      show that ABS generally outperforms previous approaches in terms of 
      accuracy when used to identify Blobs.}
}

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