Empirical evaluation of the effects of mixed project data on learning defect predictors

Turhan B., Misirli A. T., Bener A.

INFORMATION AND SOFTWARE TECHNOLOGY, cilt.55, sa.6, ss.1101-1118, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 55 Sayı: 6
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1016/j.infsof.2012.10.003
  • Dergi Adı: INFORMATION AND SOFTWARE TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1101-1118
  • İstanbul Teknik Üniversitesi Adresli: Hayır

Özet

Context: Defect prediction research mostly focus on optimizing the performance of models that are constructed for isolated projects (i.e. within project (WP)) through retrospective analyses. On the other hand, recent studies try to utilize data across projects (i.e. cross project (CP)) for building defect prediction models for new projects. There are no cases where the combination of within and cross (i.e. mixed) project data are used together.