Journal of Fuzzy Logic and Modeling in Engineering - Volume 2, Issue 1, 2024

Software security evaluation focuses on the fundamental security features of the software as well as the quantum enable security alternatives . The rapid development of a number of qubits in quantum computers makes the present security mechanism of software insecure. The software security evaluation is the most crucial part of surveying, controlling, and administering security in order to further improve the properties of safety.

It's crucial to understand that performing a security assessment early on in the development process can help you find bugs, vulnerabilities, faults, and attacks. In this quantitative study, the definition and use of the quantum computing security approach in software security will be covered. The cryptographic calculations had to secure our institutions based on computers and networks.

The Fuzzy Technique for Order Preference by Similarity to Ideal Situation (Fuzzy-TOPSIS) to quantitatively assess the rank of the quantum enable security alternatives with security factors.

The Quantum Key Distribution [A2], the quantum technique of security approach, has got the top priority and quantum key distribution in GHz state [A6] got the least in the estimation of software security during the era of quantum computer by the neural network method of Fuzzy-TOPSIS.

The quantum mechanism of computing makes classical computing insecure. The security estimation of software makes developers focus on the quantum mechanism of security. The quantum mechanism of quantum key distribution is to make software secure.

In this study, a review of fuzzy implementation to Real Options Approach (ROA) theory where the applicability of classical and extended theories of “fuzziness” studied.

ROA allows taking into account the value of some sources of managerial flexibility and therefore assessing a more accurately project value. The positive value of flexibility results from limiting the impacts of adverse events while taking advantage of positive ones. One of the main lessons is that uncertainty adds value in the presence of flexibility. Ambiguous parameters that have a significant effect on the project value are usually represented as fuzzy sets using Zadeh's classical theory of Fuzzy logic (also termed “type-1”). However, there have been so many derivatives, and expansions of the fuzzy set theories developed by different researchers. Dealing with uncertainty can be manifested in the different mechanism of fuzziness.

The objective of this review is to identify the research gap as well as provide an elementary guide to the applicability of different varieties of classical and extended applicability of fuzziness to ROA when evaluating project investment.

After a generic review of the progress of ROA theory and fuzzy approaches by researchers This paper reviews the applicability of ROA to fuzzy sets (classical and extended) implementation to decision-making for large projects where project timing and uncertainty are key parameters affecting the project value

After reviewing the applicability of each of the classical and extended theories of fuzzy logic to ROA, a tabular format shows the result of this study summarizing the scenario, showing the applicability of different techniques.

Most of the reviewed techniques of fuzzy implementation to ROA approach, still based on the classical theory of fuzzy logic. Implementation of more extended techniques has a potential of enhancing the outcome of such research.