Empirical evaluation of mixed-project defect prediction models

Burak Turhan*, Ayse Tosun, Ayşe Bener

*Bu çalışma için yazışmadan sorumlu yazar

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26 Atıf (Scopus)

Özet

Defect prediction research mostly focus on optimizing the performance of models that are constructed for isolated projects. On the other hand, recent studies try to utilize data across projects for building defect prediction models. We combine both approaches and investigate the effects of using mixed (i.e. within and cross) project data on defect prediction performance, which has not been addressed in previous studies. We conduct experiments to analyze models learned from mixed project data using ten proprietary projects from two different organizations. We observe that code metric based mixed project models yield only minor improvements in the prediction performance for a limited number of cases that are difficult to characterize. Based on existing studies and our results, we conclude that using cross project data for defect prediction is still an open challenge that should only be considered in environments where there is no local data collection activity, and using data from other projects in addition to a project's own data does not pay off in terms of performance.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıProceedings - 37th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2011
Sayfalar396-403
Sayfa sayısı8
DOI'lar
Yayın durumuYayınlandı - 2011
Harici olarak yayınlandıEvet
Etkinlik37th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2011 - Oulu, Finland
Süre: 30 Ağu 20112 Eyl 2011

Yayın serisi

AdıProceedings - 37th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2011

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???event.eventtypes.event.conference???37th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2011
Ülke/BölgeFinland
ŞehirOulu
Periyot30/08/112/09/11

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