Quantitative Analysis of Development Environment Risk for Agile Software through Machine Learning
Anand Kumar Rai1, Shalini Agarwal2, Mazahar Khaliq3, Abhishek Kumar4

1Mr. Anand Kumar Rai, Research Scholar, Shri Ramswaroop Memorial University, Computer Science & Engineering, Barabanki, (U.P), India.
2Dr. Shalini Agarwal, Associate Professor, Shri Ramswaroop Memorial University, Computer Science & Engineering, Barabanki, (U.P), India.
3Dr. Mazahar Khaliq, Assistant Professor, Computer Application, KMC Urdu Arabi Farsi University, Lucknow, (U.P), India.
4Dr. Abhishek Kumar, Assistant Professor, Department of Computer science, Banaras Hindu University, BHU, Varanasi, (U.P), India.

Manuscript received on 13 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 83-89 | Volume-7 Issue-6, March 2019 | Retrieval Number: E2110017519/19©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: Agile methodology practice has increased in today’s era of software industries. In this study the 9 risk elements of the agile software development environment have been identified. The qualitative value of risk elements have been converted into the quantitative form with the help of a fuzzy inference system. These quantitative values have been used to train the back propagation network. This study will contribute significantly in reducing risks in the use of the agile methodology, because the risks are accurately expressed in a quantitative way. This study has been performed on the software projects made on agile methods XP and Scrum with the help of MATLAB simulator.
Keywords: Agile Software, AI Learning, Back Propagation Network, Fuzzy Inference System.
Scope of the Article: Deep Learning