A novel deep neural network structure for software fault prediction.

ABSTRACT: Software fault prediction is crucial to compute the potential occurrence of faults within the software components, before code testing or execution. Machine learning, especially deep learning, has been applied to predict faults, but both encounter challenges such as insufficient accuracy, imbalanced data, overfitting, and complex structure. Moreover, deep learning yields superior predictions when working with large datasets. The most common fault dataset is NASA MDP from the PROMISE repository. The BugHunter dataset, however, offers a larger number of instances compare to other fault datasets, leaving a gap in the literature for exploring the application of machine learning and deep learning. In this study, we present a novel structure of deep neural network (DNN), which utilizes the convo

SUBMITTER: Modanlou Jouybari M 

PROVIDER: S-EPMC11623021 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature