Abstract
The heart of research aims to develop an algorithmic paradigm for risk mitigation in software projects by using Neuro-Fuzzy techniques. We have identified the requirements for developing the prototype of required tool which will help in determining the risk level of the project. Various approaches of Artificial Intelligence have been discussed in detail for risk assessment and mitigation in past . The algorithm that has been designed can be implemented using MATLAB or Java Frame works. The system utilizes Fuzzy logic to build the fuzzy inference system which focuses on the production of the membership function which is used for the Fuzzification and Defuzzification process. The training of system is done by using back propagation and Bayesian regulation approach of the neural networks. Neuro-Fuzzy technique has been applied to analyze the risk for dealing with uncertainty and incomplete specifications. The performance can have better results for neuro fuzzy model if we use Bayesian regulation approach as compare to the Back propagation because of low prediction capabilities in Back propagation.
Highlights
Management of various risk factors is one of the most important part of project management .Because of high growth in internet market and technical advancement software professionals face various security threats
Division of the research has been done into various sections,where section 1 introduces about the research aims, section 2 outputs the literature in past,section 3 discusses the proposed algorithm,section 4 describes the experimental analysis and tool prototype for risk mitigation and we conclude in the section 5
The main aim of providing the best result in limited and finite resource here risk based group is divided into two parts that is fault prone (FP) and not fault prone (NFP) and Type I and Type II error is discus that is Type I error occurs when a nfp module is misclassified as fp, whereas a Type II error occurs when a fp module is misclassified as nfp using Genetic Programming (GP) with the proposed model is achieved a good performance and optimizing the quality of software by telling us whether the software is fault prone or not faultprone
Summary
Management of various risk factors is one of the most important part of project management .Because of high growth in internet market and technical advancement software professionals face various security threats. The consequence of natural or man-made disasters to organizations can have adverse effects on organizations in terms of finance, in terms of organization image as well as its image. It can effect organization relations with its clients and the required market. We require contingency plan for reducing risk if it becomes an outcome for a software project. Every organization has its primary goal to reduce security related risks in the software projects. Division of the research has been done into various sections ,where section 1 introduces about the research aims, section 2 outputs the literature in past ,section 3 discusses the proposed algorithm ,section 4 describes the experimental analysis and tool prototype for risk mitigation and we conclude in the section 5
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