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<p>Since the 1970s, the study and enhancement of software reliability growth models (SRGMs) have attracted substantial attention from the scholarly community. Several models with varying presumptions have been proposed for improving the reliability of the software, and still, research is going on. In this paper, a new SRGM is proposed incorporating the correction lag and fault removal efficiency, and the discussed model is compared by three optimization techniques, Particle swarm optimization (PSO), Grey wolf optimization (GWO), and a hybrid approach combining PSO and GWO (HPSGWO) algorithm.The experimental findings are based on the CEC-2019 benchmark functions, and the proposed parameter optimization approach is carried out utilizing an HPSGWO algorithm that is highly effective and adaptable, leading to improved software reliability growth results.</p>

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Enhanced software reliability prediction using a hybrid PSO-GWO algorithm

  • 期刊论文

  • 发表日期：

    2026年5月22日

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  • Anup Kumar Behera,
  • Priyanka Agarwal

摘要

Since the 1970s, the study and enhancement of software reliability growth models (SRGMs) have attracted substantial attention from the scholarly community. Several models with varying presumptions have been proposed for improving the reliability of the software, and still, research is going on. In this paper, a new SRGM is proposed incorporating the correction lag and fault removal efficiency, and the discussed model is compared by three optimization techniques, Particle swarm optimization (PSO), Grey wolf optimization (GWO), and a hybrid approach combining PSO and GWO (HPSGWO) algorithm.The experimental findings are based on the CEC-2019 benchmark functions, and the proposed parameter optimization approach is carried out utilizing an HPSGWO algorithm that is highly effective and adaptable, leading to improved software reliability growth results.