- Tytuł:
- A deep ensemble learning method for effort-aware just-in-time defect prediction
- Autorzy:
- Albahli, Saleh
- Powiązania:
- https://bibliotekanauki.pl/articles/117652.pdf
- Data publikacji:
- 2020
- Wydawca:
- Polskie Towarzystwo Promocji Wiedzy
- Tematy:
-
Deep Neural Network
unlabeled dataset
Just-In-Time defect prediction
unsupervised prediction
nieoznakowany zbiór danych
przewidywanie defektów Just-In-Time
przewidywanie bez nadzoru - Opis:
- Since the introduction of Just-in-Time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods for defect prediction. To predict the changes that are defect-inducing, it is im-portant for learning model to consider the nature of the dataset, its imbalance properties and the correlation between different attributes. In this paper, we evaluated the importance of dataset properties, and proposed a novel methodology for learning the effort aware just-in-time defect prediction model. We form an ensemble classifier, which consider the output of three individuals classifier i.e. Random forest, XGBoost and Deep Neural Network. Our proposed methodology shows better performance with 77% accuracy on sample dataset and 81% accuracy on different dataset.
- Źródło:
-
Applied Computer Science; 2020, 16, 3; 5-15
1895-3735 - Pojawia się w:
- Applied Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł