  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.
