An Effective Software Risk Prediction Management Analysis of Data Using Machine Learning and Data Mining Method

TL;DR

This paper introduces a novel risk prediction approach combining machine learning, data mining, and fuzzy techniques, validated on benchmark datasets, to improve software risk management accuracy and effectiveness.

Contribution

It presents an integrated risk assessment method using enhanced fuzzy techniques and deep learning models, optimized with a new algorithm, for better software project risk prediction.

Findings

Outperforms existing methods in risk evaluation accuracy.

Validated on NASA 93 dataset with promising results.

Effective in both closed-world and open-world scenarios.

Abstract

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.