@INPROCEEDINGS{Abebe12-WCRE-LBS,
   AUTHOR       = {Surafel Lemma Abebe and Paolo Tonella and 
      Venera Arnaoudova and Giuliano Antoniol and Yann-Ga�l Gu�h�neuc},
   BOOKTITLE    = {Proceedings of the 19<sup>th</sup> Working Conference on Reverse Engineering (WCRE)},
   TITLE        = {Can Lexicon Bad Smells Improve Fault Prediction?},
   YEAR         = {2012},
   OPTADDRESS   = {},
   OPTCROSSREF  = {},
   EDITOR       = {Rocco Oliveto and Denys Poshyvanyk},
   MONTH        = {October},
   NOTE         = {10 pages.},
   OPTNUMBER    = {},
   OPTORGANIZATION = {},
   PAGES        = {235--244},
   PUBLISHER    = {IEEE CS Press},
   OPTSERIES    = {},
   OPTVOLUME    = {},
   KEYWORDS     = {Topic: <b>Code and design smells</b>, 
      Venue: <c>WCRE</c>},
   URL          = {http://www.ptidej.net/publications/documents/WCRE12d.doc.pdf},
   PDF          = {http://www.ptidej.net/publications/documents/WCRE12d.ppt.pdf},
   ABSTRACT     = {In software development, early identification of 
      fault-prone classes can save a considerable amount of resources. In 
      the literature, source code structural metrics have been widely 
      investigated as one of the factors that can be used to identify 
      faulty classes. Structural metrics measure code complexity, one 
      aspect of the source code quality. Complexity might affect program 
      understanding and hence increase the likelihood of inserting errors 
      in a class. Besides the structural metrics, we believe that the 
      quality of the identifiers used in the code may also affect program 
      understanding and thus increase the likelihood of error insertion. In 
      this study, we measure the quality of identifiers using the number of 
      Lexicon Bad Smells (LBS) they contain. We investigate whether using 
      LBS in addition to structural metrics improves fault prediction. To 
      conduct the investigation, we asses s the prediction capability of a 
      model while using i) only structural metrics, and ii) structural 
      metrics and LBS. The results on three open source systems, ArgoUML, 
      Rhino, and Eclipse, indicate that there is an improvement in the 
      majority of the cases.}
}

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L.","Tonella, P.","Arnaoudova, V.","Antoniol, G.","Gu�h�neuc, Y."],"year":2012,"bibtype":"inproceedings","biburl":"http://www.yann-gael.gueheneuc.net/Work/Publications/Biblio/complete-bibliography.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Surafel","Lemma"],"propositions":[],"lastnames":["Abebe"],"suffixes":[]},{"firstnames":["Paolo"],"propositions":[],"lastnames":["Tonella"],"suffixes":[]},{"firstnames":["Venera"],"propositions":[],"lastnames":["Arnaoudova"],"suffixes":[]},{"firstnames":["Giuliano"],"propositions":[],"lastnames":["Antoniol"],"suffixes":[]},{"firstnames":["Yann-Ga�l"],"propositions":[],"lastnames":["Gu�h�neuc"],"suffixes":[]}],"booktitle":"Proceedings of the 19^th Working Conference on Reverse Engineering (WCRE)","title":"Can Lexicon Bad Smells Improve Fault Prediction?","year":"2012","optaddress":"","optcrossref":"","editor":[{"firstnames":["Rocco"],"propositions":[],"lastnames":["Oliveto"],"suffixes":[]},{"firstnames":["Denys"],"propositions":[],"lastnames":["Poshyvanyk"],"suffixes":[]}],"month":"October","note":"10 pages.","optnumber":"","optorganization":"","pages":"235–244","publisher":"IEEE CS Press","optseries":"","optvolume":"","keywords":"Topic: <b>Code and design smells</b>, Venue: <c>WCRE</c>","url":"http://www.ptidej.net/publications/documents/WCRE12d.doc.pdf","pdf":"http://www.ptidej.net/publications/documents/WCRE12d.ppt.pdf","abstract":"In software development, early identification of fault-prone classes can save a considerable amount of resources. In the literature, source code structural metrics have been widely investigated as one of the factors that can be used to identify faulty classes. Structural metrics measure code complexity, one aspect of the source code quality. Complexity might affect program understanding and hence increase the likelihood of inserting errors in a class. Besides the structural metrics, we believe that the quality of the identifiers used in the code may also affect program understanding and thus increase the likelihood of error insertion. In this study, we measure the quality of identifiers using the number of Lexicon Bad Smells (LBS) they contain. We investigate whether using LBS in addition to structural metrics improves fault prediction. To conduct the investigation, we asses s the prediction capability of a model while using i) only structural metrics, and ii) structural metrics and LBS. The results on three open source systems, ArgoUML, Rhino, and Eclipse, indicate that there is an improvement in the majority of the cases.","bibtex":"@INPROCEEDINGS{Abebe12-WCRE-LBS,\r\n AUTHOR = {Surafel Lemma Abebe and Paolo Tonella and \r\n Venera Arnaoudova and Giuliano Antoniol and Yann-Ga�l Gu�h�neuc},\r\n BOOKTITLE = {Proceedings of the 19^th Working Conference on Reverse Engineering (WCRE)},\r\n TITLE = {Can Lexicon Bad Smells Improve Fault Prediction?},\r\n YEAR = {2012},\r\n OPTADDRESS = {},\r\n OPTCROSSREF = {},\r\n EDITOR = {Rocco Oliveto and Denys Poshyvanyk},\r\n MONTH = {October},\r\n NOTE = {10 pages.},\r\n OPTNUMBER = {},\r\n OPTORGANIZATION = {},\r\n PAGES = {235--244},\r\n PUBLISHER = {IEEE CS Press},\r\n OPTSERIES = {},\r\n OPTVOLUME = {},\r\n KEYWORDS = {Topic: <b>Code and design smells</b>, \r\n Venue: <c>WCRE</c>},\r\n URL = {http://www.ptidej.net/publications/documents/WCRE12d.doc.pdf},\r\n PDF = {http://www.ptidej.net/publications/documents/WCRE12d.ppt.pdf},\r\n ABSTRACT = {In software development, early identification of \r\n fault-prone classes can save a considerable amount of resources. In \r\n the literature, source code structural metrics have been widely \r\n investigated as one of the factors that can be used to identify \r\n faulty classes. Structural metrics measure code complexity, one \r\n aspect of the source code quality. Complexity might affect program \r\n understanding and hence increase the likelihood of inserting errors \r\n in a class. Besides the structural metrics, we believe that the \r\n quality of the identifiers used in the code may also affect program \r\n understanding and thus increase the likelihood of error insertion. In \r\n this study, we measure the quality of identifiers using the number of \r\n Lexicon Bad Smells (LBS) they contain. We investigate whether using \r\n LBS in addition to structural metrics improves fault prediction. To \r\n conduct the investigation, we asses s the prediction capability of a \r\n model while using i) only structural metrics, and ii) structural \r\n metrics and LBS. The results on three open source systems, ArgoUML, \r\n Rhino, and Eclipse, indicate that there is an improvement in the \r\n majority of the cases.}\r\n}\r\n\r\n","author_short":["Abebe, S. 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