Elife ÖZTÜRK KIYAK, KÖKTEN ULAŞ BİRANT, DERYA BİRANT

Yazılım Hata Tahmini için Sıralı Sınıflandırma Yaklaşımı

Yazılım hata tahmini, kaynak kodda bulunan olası hataların (veya kusurların) varlığını tahmin etmek için sınıflandırma ve/veya regresyon algoritmalarının kullanımı işlemidir. Fakat, literatürde bulunan sınıflandırma çalışmaları, veri setlerindeki hedef özellik değerlerini iki olasılıklı (hatalı veya hatasız) veya sırasız olarak kabul etmektedir. Bu nedenle; sıfır, az veya çok hatalı gibi sınıf değerleri arasındaki sıralama mantığını değerlendirmemektedir. Bu eksikliği gidermek amacıyla, bu çalışma, yazılım hata tahminleme problemi için sıralı sınıflandırma metotlarını kullanan yeni bir yaklaşım önermektedir. Makalede, çeşitli sınıflandırma algoritmalarının (rastgele orman, destek vektör makineleri, Naive Bayes ve k-en yakın komşu) sıralı ve itibari sürümleri, yazılım mühendisliği alanındaki 38 gerçek veriseti üzerinde sınıflandırma performansları açısından karşılaştırılmıştır. Sonuçlar, sıralı sınıflandırma yaklaşımının geleneksel (itibari) çözümlere nispeten ortalamada daha iyi bir sınıflandırma doğruluğuna ulaştığını göstermektedir.

Anahtar Kelimeler:

Yazılım hata tahmini, Sıralı sınıflandırma, Yazılım mühendisliği, Yazılım kalitesi

An Ordinal Classification Approach for Software Bug Prediction

Software bug prediction is the process of utilizing classification and/or regression algorithms to predict the presence of possible errors (or defects) in a source code. However, current classification studies in the literature assume that the target attribute values in the datasets are binary (i.e. buggy or non-buggy) or unordered, so they lose inherent order between the class values such as zero, less and more bug levels. To overcome this drawback, this study proposes a novel approach which suggests ordinal classification methods as a solution for software bug prediction problem. This article compares ordinal and nominal versions of various classification algorithms (random forest, support vector machine, Naive Bayes and k-nearest neighbor) in terms of classification performance on real-world 38 software engineering datasets. The results indicate that ordinal classification approach achieves better classification accuracy on average than the traditional (nominal) solutions.

Keywords:

Software bug prediction, Ordinal classification, Software engineering, Software quality,

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