In the software industry, two software engineering development best practices coexist: open-source and closed-source software. The former has a shared code that anyone can contribute, whereas the latter has a proprietary code that only the owner can access. Software reliability is crucial in the industry when a new product or update is released. Applying meta-heuristic optimization algorithms for closed-source software reliability prediction has produced significant and accurate results. Now, open-source software dominates the landscape of cloud-based systems. Therefore, providing results on open-source software reliability - as a quality indicator - would greatly help solve the open-source software reliability growth-modelling problem. The reliability is predicted by estimating the parameters of the software reliability models. As software reliability models are inherently nonlinear, traditional approaches make estimating the appropriate parameters difficult and ineffective. Consequently, software reliability models necessitate a high-quality parameter estimation technique. These objectives dictate the exploration of potential applications of meta-heuristic swarm intelligence optimization algorithms for optimizing the parameter estimation of nonhomogeneous Poisson process-based open-source software reliability modelling. The optimization algorithms are firefly, social spider, artificial bee colony, grey wolf, particle swarm, moth flame, and whale. The applicability and performance evaluation of the optimization modelling approach is demonstrated through two real open-source software reliability datasets. The results are promising.

翻译：在软件行业中，存在两种共存的软件工程开发最佳实践：开源软件与闭源软件。前者具有可共享的代码，任何人都能参与贡献；而后者拥有专有代码，仅所有者有权访问。当发布新产品或更新版本时，软件可靠性在行业中至关重要。应用元启发式优化算法进行闭源软件可靠性预测已取得显著且精准的结果。如今，开源软件主导着云系统的格局。因此，将开源软件可靠性作为质量指标的研究成果，将极大有助于解决开源软件可靠性增长建模难题。可靠性预测通过估计软件可靠性模型的参数实现。由于软件可靠性模型本质上是非线性的，传统方法难以有效估计合适参数，因此软件可靠性模型需要高质量的参数估计技术。这些目标促使我们探索元启发式群体智能优化算法在非齐次泊松过程开源软件可靠性建模参数优化中的应用潜力。研究所采用的优化算法包括：萤火虫算法、社会蜘蛛算法、人工蜂群算法、灰狼算法、粒子群算法、蛾焰算法及鲸鱼算法。通过两个真实开源软件可靠性数据集，验证了优化建模方法的适用性与性能评估，结果令人满意。