Kosinüs Benzerliğine Dayalı Çapraz-proje Hata Tahmini

Year 2019, , 159 - 167, 31.07.2019

Muhammed Maruf Öztürk

https://doi.org/10.17671/gazibtd.453436

Abstract

Çapraz-proje tahmini özellikle metrik heterojenliği açısından
araştırmacıların ilgisini çekmekte, bu alanda yeni yöntemlere ihtiyaç
duyulmaktadır. Hata tahmin işleminin farklı projeler üzerinden yürütülmesi
geliştiricilere anlamlı bilgiler sunmaktadır. Bu çalışmada, çapraz-proje
tahmini için, Kosinüs benzerliğine dayalı metrik eşleştirmesi yapan CSCDP
isimli bir algoritma geliştirilmiştir. Yöntem 36 farklı veri setinde üç farklı
sınıflandırıcı ile test edilmiştir.  Elde
edilen sonuçlara göre ortalama tahmin performansının yapay sinir ağlarında
diğer sınıflandırıcılara göre daha yüksek olduğu tespit edilmiştir. Ayrıca,
seyreklik analizine dayalı olarak seçilen eğitim veri setlerinin test
başarısını olumlu etkilediği tespit edilmiştir. Son olarak, CSCDP kullanılarak yürütülen
çapraz-proje tahmininin sınıflandırma hatasını Random Forest algoritmasında
F-skor parametresi için 0.65 oranında azalttığı gözlemlenmiştir.

Keywords

hata tahmini, kosinüs benzerliği, çapraz-proje

Cosine Similarity-based Cross-project Defect Prediction

Abstract

Cross-project
defect prediction has been intriguing researchers in terms of metric
heterogeneity and new methods are needed in this field. Performing defect
prediction through different projects presents valuable information for
developers. In this work, a metric matching algorithm namely CSCDP is presented
for cross-project defect prediction. The method is then tested on 36 different
projects via three classifiers. According to the obtained results, neural
network predictor outperforms the others in terms of mean prediction values. Further,
selecting training data sets using sparsity analysis creates a favorable effect
on testing performance. Last, CSCDP was able to reduce classification error up
to 0.65 in Random Forest for F-score.

Keywords

defect prediction, consine-similarity, cross-project

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