Risk Assessment Strategy Performance Measure Using Confusion Matrix
Kunamneni Avinash1, Betha Yasaswi2, D Naga Malleswari3
1Kunamneni Avinash, Student, Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram (Andhra Pradesh), India.
2Betha Yasaswi, Student, Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram (Andhra Pradesh), India.
3D Naga Malleswari, Assistant Professor, Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram (Andhra Pradesh), India.
Manuscript received on 22 April 2019 | Revised Manuscript received on 04 May 2019 | Manuscript Published on 17 May 2019 | PP: 90-92 | Volume-7 Issue-6S4 April 2019 | Retrieval Number: F10180476S419/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Software Risk Evaluation is a software engineering process for identification, risk estimation and developing mitigation strategies for risks in software intensive system. Risk assessment is incorporation of two tasks risk analysis and risk management, i.e., it identifies risks through systematic processes and as certain the consequences, and then see how to diminish the risks. There are number of risk evaluation methodologies that specialize in various categories of risk andmultiple areas of concern. Risk assessment means to completely learn about the degree of the risks, prioritize the risks and categorize the risks. In this paper we propose a software risk assessment using a machine learning and specifically the problem of statistical classification. We have used a classification model Confusion Matrix because it requires correct description of the target system. We have used the confusion matrix to identify and assess the risk.
Keywords: Risk, Risk Assessment, Risk Management, Confusion Matrix.
Scope of the Article: High Performance Computing