For one to guarantee higher-quality software development processes, risk management is essential. Furthermore, risks are those that could negatively impact an organization's operations or a project's progress. The appropriate prioritisation of software project risks is a crucial factor in ascertaining the software project's performance features and eventual success. They can be used harmoniously with the same training samples and have good complement and compatibility. We carried out in-depth tests on four benchmark datasets to confirm the efficacy of our CIA approach in closed-world and open-world scenarios, with and without defence. We also present a sequential augmentation parameter optimisation technique that captures the interdependencies of the latest deep learning state-of-the-art WF attack models. To achieve precise software risk assessment, the enhanced crow search algorithm (ECSA) is used to modify the ANFIS settings. Solutions that very slightly alter the local optimum and stay inside it are extracted using the ECSA. ANFIS variable when utilising the ANFIS technique. An experimental validation with NASA 93 dataset and 93 software project values was performed. This method's output presents a clear image of the software risk elements that are essential to achieving project performance. The results of our experiments show that, when compared to other current methods, our integrative fuzzy techniques may perform more accurately and effectively in the evaluation of software project risks.

翻译：为确保软件开发过程的高质量，风险管理至关重要。风险指可能对组织运营或项目进展产生负面影响的不确定性因素。软件项目风险的合理优先级排序是决定软件项目性能特征与最终成功的关键要素。它们能够使用相同的训练样本协同工作，具有良好的互补性与兼容性。我们在四个基准数据集上进行了深入测试，以验证所提出的CIA方法在封闭世界与开放世界场景中的有效性，涵盖有防御与无防御两种情况。同时，我们提出了一种序列增强参数优化技术，该技术能够捕捉最新深度学习前沿网站指纹攻击模型间的相互依赖关系。为实现精准的软件风险评估，采用增强型乌鸦搜索算法对ANFIS参数进行优化。ECSA能够提取对局部最优解扰动极小且保持在其内部的解决方案。通过ANFIS技术对ANFIS变量进行优化。使用NASA 93数据集及93个软件项目值进行了实验验证。该方法输出结果清晰呈现了影响项目绩效的关键软件风险要素。实验结果表明，与现有其他方法相比，我们提出的集成模糊技术在软件项目风险评估中能够实现更高精度与更优性能。