Development of Rule-Based Software Risk Assessment and Management Method with Fuzzy Inference System

Mustafa Batar,Kökten Ulaş Birant,Ali Hakan Işık,Sikandar Ali

doi:10.1155/2021/5532197

Mustafa Batar, Kökten Ulaş Birant + Show 2 more

Open Access

https://doi.org/10.1155/2021/5532197

Copy DOI

Abstract

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.

Highlights

Risk is the likelihood of not reaching a targeted result, and it is the probability of any event that would prevent an organization from achieving its strategic, financial, and operational objectives
Risks come on the whole shapes and sizes; hazard experts, by and large, perceive three significant sorts: market risk is the danger that costs will move in a manner that has antagonistic outcomes of an organization; credit risk is the danger that a client, a counterparty, or a provider will neglect to meet its commitments; and operational risk is the danger that individuals, cycles, or frameworks will fall flat or that an outer occasion will contrarily affect the organization [2]
The rule set has been applied into the Adaptive Neuro-Fuzzy Inference System (ANFIS) (Adaptive NeuroFuzzy Inference System) method in order to design and develop new and original software risk rules on MATLAB

Summary

Introduction

Risk is the likelihood of not reaching a targeted result, and it is the probability of any event that would prevent an organization from achieving its strategic, financial, and operational objectives. It means that the fuzzy inference system has provided trustworthy results in software risk assessment and management with a high accuracy rate. Ese issues have been team support, team loyalty, team vision, team personalities, team meeting, team members, and team leader They have tried to find answers to some questions about software risk assessment and management. Ese questions have been related to the number of constructs, code changes, method (method) number, fixed code parts, work-life, work quality, team leader, software project documents, and software development tool.

Fuzzy Logic

Findings

Conclusions and Future