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.

利用 CIA 方法和改进的软件风险评估算法（ECSA）进行软件项目风险评估，使用深度学习和模糊技术，在评估软件项目风险方面表现更准确和有效。

一种利用机器学习和数据挖掘方法进行有效软件风险预测管理分析的研究

An effective software risk prediction management analysis of data using  machine learning and data mining method

This study explores the application of deep learning technologies in software
development processes, particularly in automating code reviews, error
prediction, and test generation to enhance code quality and development
efficiency. Through a series of empirical studies, experimental groups using
deep learning tools and control groups using traditional methods were compared
in terms of code error rates and project completion times. The results
demonstrated significant improvements in the experimental group, validating the
effectiveness of deep learning technologies. The research also discusses
potential optimization points, methodologies, and technical challenges of deep
learning in software development, as well as how to integrate these
technologies into existing software development workflows.

深度学习技术在软件开发过程中的应用，特别是在自动化代码审查、错误预测和测试生成，以提高代码质量和开发效率方面进行了研究。通过一系列的实证研究，比较了使用深度学习工具的实验组和使用传统方法的对照组在代码错误率和项目完成时间方面的差异。结果表明实验组有明显改善，验证了深度学习技术的有效性。研究还讨论了深度学习在软件开发中的潜在优化点、方法论和技术挑战，以及如何将这些技术融入现有的软件开发工作流程。