There is an enormous budget and financial plan in software development projects, and it is required that they take a huge investment to carry on. When looked at, the costs depend on the global substantial information about software development: in 1985, $150 billion; in 2010, $2 trillion; in 2015, $5 trillion; and in 2020, over $7 trillion. Additionally, on the first new days of 2021, a day-by-day Apple Store’s quantity has been approximately $500 million. In spite of the expenditures and the margins that are dramatically expanding and increasing each year, the phase of software development accomplishment is not high enough. In light of the “CHAOS” report arranged in 2015, just 17% of the software projects were finished in an opportune way, in the allotted financial plan, and as per the necessities. However, 53% of the software projects were finished in the long run or potentially over a spending plan as well as without satisfying the prerequisites precisely. In addition, software development projects were not completed and were dropped out as well in the ratio of 30%. Also, the “CHAOS” report published in 2020 has figured out that only 33% of the software projects were completed successfully all over the world. In order to cope with these unsuccessful and failure results, an effective method for software risk assessment and management has to be specified, designated, and applied. In this way, before causing trouble that has the power of preventing successful accomplishment of software development projects, software risks are able to be noticed and distinguished on time. In this study, a new and original rule set, which could be used and carried out effectively in software risk assessment and management, has been designed and developed based on the implementation of fuzzy approached technique integrated with machine learning algorithm—Adaptive Neuro-Fuzzy Inference System (ANFIS). By this approach and technique, machines (computers) are able to create several software risk rules not to be seen, not to be recognized, and not to be told by human beings. In addition, this fuzzy inference approach aims to decrease risks in the software development process in order to increase the success rate of the software projects. Also, the experimental results of this approach show that rule-based software risk assessment and management method has a valid and accurate model with a high accuracy rate and low average testing error.

中文翻译：

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基于规则的模糊推理软件风险评估与管理方法的开发

软件开发项目中有庞大的预算和财务计划，并且要求他们进行大量投资才能继续进行。从成本的角度看，费用取决于有关软件开发的全球实质性信息：1985年为1500亿美元；2010年为2万亿美元；2015年为5万亿美元；到2020年，将超过7万亿美元。此外，在2021年的头几天，Apple Store的日销售额约为5亿美元。尽管支出和利润每年都在急剧增加和增加，但是软件开发成就的阶段还不够高。根据2015年安排的“ CHAOS”报告，只有17％的软件项目是在适当的时候，根据分配的财务计划以及根据需要完成的。然而，53％的软件项目是在长期内完成的，或者有可能超出支出计划，并且没有完全满足先决条件。此外，软件开发项目尚未完成，也以30％的比例退出。此外，2020年发布的“ CHAOS”报告指出，全世界仅成功完成了33％的软件项目。为了应对这些不成功和失败的结果，必须指定，指定和应用一种有效的软件风险评估和管理方法。这样，在引起具有阻止软件开发项目成功完成的能力的麻烦之前，可以及时发现和区分软件风险。在这项研究中，新的和原始的规则集 在结合机器学习算法-自适应神经模糊推理系统（ANFIS）的模糊逼近技术的实现的基础上，设计并开发了可在软件风险评估和管理中有效使用和实施的软件。通过这种方法和技术，机器（计算机）能够创建一些不可见，不被识别以及不被人类告知的软件风险规则。此外，这种模糊推理方法旨在降低软件开发过程中的风险，以提高软件项目的成功率。此外，该方法的实验结果表明，基于规则的软件风险评估和管理方法具有有效且准确的模型，具有较高的准确率和较低的平均测试误差。