1 Ocak 2009 Konferans bildirisi Açık Erişim

Catal, C.; Sevim, U.; Diri, B.

Dublin Core

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  <dc:creator>Catal, C.</dc:creator>
  <dc:creator>Sevim, U.</dc:creator>
  <dc:creator>Diri, B.</dc:creator>
  <dc:date>2009-01-01</dc:date>
  <dc:description>Software metrics and fault data belonging to a previous software version are used to build the software fault prediction model for the next release of the software. Until now, different classification algorithms have been used to build this kind of models. However, there are cases when previous fault data are not present; and hence, supervised learning approaches cannot be applied. In this study, we propose a fully automated technique which does not require an expert during the prediction process. In addition, it is not required to identify the number of clusters before the clustering phase, as required by K-means clustering method. Software metrics thresholds are used to remove the expert necessity.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/94297</dc:identifier>
  <dc:identifier>oai:zenodo.org:94297</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:title>Software Fault Prediction of Unlabeled Program Modules</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
</oai_dc:dc>