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


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

INFORMATION AND SOFTWARE TECHNOLOGY, vol.55, no.6, pp.1101-1118, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 55 Issue: 6
  • Publication Date: 2013
  • Doi Number: 10.1016/j.infsof.2012.10.003
  • Journal Name: INFORMATION AND SOFTWARE TECHNOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1101-1118
  • Istanbul Technical University Affiliated: No

Abstract

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.