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Description
  • Predicting the severity of bugs has been found in past research to improve triaging and the bug resolution process. For this reason, many classification/prediction approaches emerged over the years to provide an automated reasoning over severity classes. In this paper, we use text mining together with bi-grams and feature selection to improve the classification of bugs in severe/non-severe classes. We adopt the Naive Bayes (NB) classifier considering Mozilla and Eclipse datasets commonly used in related works. Overall, the results show that the application of bi-grams can improve slightly the performance of the classifier, but feature selection can be more effective to determine the most informative terms and bi-grams. The results are in any case project-dependent, as in some cases the addition of bi-grams may worsen the performance.
  • Predicting the severity of bugs has been found in past research to improve triaging and the bug resolution process. For this reason, many classification/prediction approaches emerged over the years to provide an automated reasoning over severity classes. In this paper, we use text mining together with bi-grams and feature selection to improve the classification of bugs in severe/non-severe classes. We adopt the Naive Bayes (NB) classifier considering Mozilla and Eclipse datasets commonly used in related works. Overall, the results show that the application of bi-grams can improve slightly the performance of the classifier, but feature selection can be more effective to determine the most informative terms and bi-grams. The results are in any case project-dependent, as in some cases the addition of bi-grams may worsen the performance. (en)
Title
  • Towards an Improvement of Bug Severity Classification
  • Towards an Improvement of Bug Severity Classification (en)
skos:prefLabel
  • Towards an Improvement of Bug Severity Classification
  • Towards an Improvement of Bug Severity Classification (en)
skos:notation
  • RIV/00216224:14330/14:00076796!RIV15-MSM-14330___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(LG13010)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
  • Rossi, Bruno
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 50807
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/14:00076796
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Bug Severity Classification; Text Mining; Feature Selection (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [53EC5AF220DA]
http://linked.open...v/mistoKonaniAkce
  • Verona
http://linked.open...i/riv/mistoVydani
  • Verona
http://linked.open...i/riv/nazevZdroje
  • 40th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2014
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Rossi, Bruno
  • Singha Roy, Nivir Kanti
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://bibframe.org/vocab/doi
  • 10.1109/SEAA.2014.51
http://purl.org/ne...btex#hasPublisher
  • IEEE
https://schema.org/isbn
  • 9781479957941
http://localhost/t...ganizacniJednotka
  • 14330
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