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Salp Swarm Optimizer for Modeling Software Reliability Prediction Problems

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Abstract

In this paper, software effort prediction (SEP) and software test prediction (STP) (i.e., software reliability problems) are tackled by integrating the salp swarm algorithm (SSA) with a backpropagation neural network (BPNN). Software effort and test prediction problems are common in software engineering and arise when seeking to determine the actual software resources needed to develop a project. BPNN is the most popular prediction algorithm used in the literature. The performance of BPNN depends totally on the initial parameter values such as weight and biases. The main objective of this paper is to integrate SSA with the BPNN to find the optimal weight for every training cycle and thereby improve prediction accuracy. The proposed method, abbreviated as SSA-BPNN, is tested on twelve SEP datasets and two STP datasets. All datasets vary in terms of complexity and size. The results obtained by SSA-BPNN are evaluated according to twelve performance measures: MSE, RMSE, RAE, RRSE, MAE, MRE, MMRE, MdMRE, VAF(%), R2(%), ED, and MD. First, the results obtained by BPNN with SSA (i.e., SSA-BPNN) and without SSA are compared. The evaluation of the results indicates that SSA-BPNN performs better than BPNN for all datasets. In the comparative evaluation, the results of SSA-BPNN are compared against thirteen state-of-the-art methods using the same SEP and STP problem datasets. The evaluation of the results reveals that the proposed method outperforms the comparative methods for almost all datasets, both SEP and STP, in the case of most performance measures. In conclusion, integrating SSA with BPNN is a very powerful approach for solving software reliability problems that can be used widely to yield accurate prediction results.

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Acknowledgements

This work was supported in part by the Ministry of Higher Education, Malaysia, under Grant FRGS /1/2019/ICT02/UKM/01/1, and in part by the Universiti Kebangsaan Malaysia under Grant DIP-2016-024.

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Authors and Affiliations

  1. Data Mining and Optimization Research Group, Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Sofian Kassaymeh, Salwani Abdullah & Zalinda Othman

  2. Al-Balqa Applied University, Salt, Jordan

    Mohamad Al-Laham

  3. Department of Computer Science, Prince Abdullah bin Ghazi Faculty of Communication and Information Technology, Al-Balqa Applied University, Salt, Jordan

    Mohammed Alweshah

  4. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, UAE

    Mohammed Azmi Al-Betar

  5. Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan

    Mohammed Azmi Al-Betar

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  1. Sofian Kassaymeh
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Kassaymeh, S., Abdullah, S., Al-Laham, M. et al. Salp Swarm Optimizer for Modeling Software Reliability Prediction Problems. Neural Process Lett 53, 4451–4487 (2021). https://doi.org/10.1007/s11063-021-10607-6

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