The effect of granularity level on software defect prediction

Gul Calikli*, Ayse Tosun, Ayse Bener, Melih Celik

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

10 Atıf (Scopus)

Özet

Application of defect predictors in software development helps the managers to allocate their resources such as time and effort more efficiently and cost effectively to test certain sections of the code. In this research, we have used Naïve Bayes Classifier (NBC) to construct our defect prediction framework. Our proposed framework uses the hierarchical structure information about the source code of the software product, to perform defect prediction at a functional method level and source file level. We have applied our model on SoftLAB and Eclipse datasets. We have measured the performance of our proposed model and applied cost benefit analysis. Our results reveal that source file level defect prediction improves the verification effort, while decreasing the defect prediction performance in all datasets.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009
Sayfalar531-536
Sayfa sayısı6
DOI'lar
Yayın durumuYayınlandı - 2009
Harici olarak yayınlandıEvet
Etkinlik2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009 - Guzelyurt, Cyprus
Süre: 14 Eyl 200916 Eyl 2009

Yayın serisi

Adı2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009

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???event.eventtypes.event.conference???2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009
Ülke/BölgeCyprus
ŞehirGuzelyurt
Periyot14/09/0916/09/09

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