An Artificially Intelligent Algorithmic Paradigm for Risk Mitigation in Risk Managed Software Testing
Vinita Malik, Sukhdip Singh
Journal of Data Processing, Vol. 8, No. 3, 2018.
DOI: https://doi.org/10.6025/jdp/2018/8/3/100-105
Link: http://www.dline.info/jdp/fulltext/v8n3/jdpv8n3_3.pdf
Abstract:
Various iterations of testing of the software are required for fulfilling specific quality parameters which further
helps in quality management of any software. The heart of this research pumps for a Neuro Fuzzy Framework that will be
utilized for mitigating software risks and such risk mitigation will lead to test case prioritization. This approach for risk
mitigation has been considered due to high accuracy levels. Here the system will be trained by Bayesian regulation approach
due to high prediction capabilities. Requirements identification has been done for the development of prototype of the tool
which will determine risk levels. Once requirements are identified then by risk analysis the testing effort will be estimated
properly for software quality improvement. The algorithmic paradigm has been developed for the proposed system which will
be implemented by MATLAB or by Java Frameworks. Fuzzy logic has been employed to make fuzzy inference system.