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Salp swarm optimizer for modeling the software fault prediction problem
S. Kassaymeh, S. Abdullah, , M. Alweshah
Published in King Saud bin Abdulaziz University
This paper proposes the salp swarm algorithm (SSA) combined with a backpropagation neural network (BPNN) to solve the software fault prediction (SFP) problem. The SFP problem is one of the well-known software engineering problems that are concerned with anticipating the software defects that are likely to appear during a software project or thereafter. In order to find the optimal BPNN parameters, a combination of SSA optimizer and BPNN named (SSA-BPNN) is proposed, so as to enhance prediction accuracy. The proposed method is evaluated against several datasets for the SFP problem. These datasets vary in both size and complexity. The results obtained are evaluated using a variety of performance measures (i.e., the AUC, Confusion Matrix, Sensitivity, Specificity, Accuracy, and Error Rate). The results obtained by SSA-BPNN are better than those obtained by the conventional BPNN over all of the datasets. The proposed method also has the ability to outperform several state-of-the-art methods over the same datasets in respect of most of the aforementioned performance measures. Therefore, the hybridization of SSA with BPNN is a valuable addition to the software engineering issues and can be utilized to achieve higher prediction accuracy for a variety of prediction problems. © 2021 The Authors
About the journal
JournalJournal of King Saud University - Computer and Information Sciences
PublisherKing Saud bin Abdulaziz University