Successful project is determined based on its effective performance and prioritization of all unavoidable software project risks. In this paper, the risk evaluation in software projects is done through developing a new hybridized fuzzy-based risk evaluation framework. During decision making process, this proposed scheme has determined and ranked all the significant project risks. Software project risks are better assessed with the incorporation of Intuitionistic fuzzy-based TOPSIS, adaptive neuro-fuzzy inference system-based multi-criteria decision making (ANFIS MCDM), and fuzzy decision making trial and evaluation laboratory methods. In order to attain accurate software risk estimation, the ANFIS parameters are adjusted with the help of enhanced crow search algorithm (ECSA). To the ANFIS approach, the ECSA is combined to make free the solutions sticking inside the local optimum and adopting only small changes for the adjustment of ANFIS parameters. NASA 93 dataset with 93 software project values was used to conduct the experimental validation. Experimental outcomes have proved evidently that the software project risks evaluation were done accurately and effectively using proposed integrated fuzzy-based framework.

成功的项目是基于其有效性能和所有不可避免的软件项目风险的优先级确定的。在本文中，软件项目中的风险评估是通过开发一种新的基于混合模糊的风险评估框架来完成的。在决策过程中，该拟议方案已确定并列出了所有重大项目风险。通过结合基于直觉模糊的 TOPSIS、基于自适应神经模糊推理系统的多标准决策 (ANFIS MCDM) 以及模糊决策试验和评估实验室方法，可以更好地评估软件项目风险。为了获得准确的软件风险估计，在增强型乌鸦搜索算法（ECSA）的帮助下调整 ANFIS 参数。对于 ANFIS 方法，结合 ECSA 使解卡在局部最优解内自由，并且仅对 ANFIS 参数的调整采用很小的变化。具有 93 个软件项目值的 NASA 93 数据集用于进行实验验证。实验结果证明，使用所提出的基于模糊集成的框架，软件项目风险评估是准确有效的。