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.
Representation of Attacker Motivation in Software Risk Assessment Using Attack Probability Trees