This study used public available data sets of software modules and provides comparative performance analysis of different machine learning techniques for software bug prediction and showed most of the machine learning methods performed well on software bug datasets.
This study used public available data sets of software modules and provides comparative performance analysis of different machine learning techniques for software bug prediction and showed most of the machine learning methods performed well on software bug datasets.
Luiz Saiqa Aleem, Fernando Capretz, Faheem Ahmed et al.· 0 citations
This study aims to improve software defect prediction five publicly available NASA datasets by using Random Forest and Classification Network to achieve higher defect prediction accuracy compared to methods without feature selection (WOFS) and to get matrix problem the authors use Classification Network.
S. G., Santosh Santosh· International Journal of Sci...· 0 citations
A review of deferent machine learning methods that are using for effort estimation like regression models, decision trees, random forest, neural networks, and then evaluate this models based on performance criteria such as MAE (Mean Absolute Error) and R 2 Score.
Montaser Fadulalla Ahmed Adam, Haroun Abdalla Eissa· 0 citations
Overall, the findings show that static code metrics can support maintainability assessment, while project-level heterogeneity remains a major challenge for reliable prediction in unseen software projects.
Fatih Şengül, Ozan Raşít Yürüm· Software quality journal· 0 citations
In the software engineering field, the size of projects is used as explanatory variable for predicting the effort, duration, defects, costs, or risks of a project. Thus, the software project size has been considered the most influential factor. A systematic mapping study on the use of categorical data in software pre...
Cuauhtémoc López-Martín, C. Yáñez-Márquez, Ali Bou Nassif· Journal of Advanced Computat...· 0 citations
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