Prediction of Defects Returning Back to Test Engineers in Data Center Stability Testing using Machine Learning Techniques
Manikandan Ramanathan1, Kumar Narayanan2
1Manikandan Ramanathan, Reserach Scholar, Department of Computer Science & Engineering, Vels Institute of Science Technology & Advanced Studies, Chennai (TamilNadu), India.
2Kumar Narayanan, Associate Professor, Department of Computer Science & Engineering, Vels Institute of Science Technology & Advanced Studies, Chennai(TamilNadu), India.
Manuscript received on 13 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript Published on 26 July 2019 | PP: 1090-1092 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F12260486S419/19©BEIESP | DOI: 10.35940/ijitee.F1226.0486S419
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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: In emerging IT industry, development of product involves quality validation by testing the product, each fault (known as defect) undergoes various stages until it gets closed in the system. In the paper we discuss the life cycle of the defect to understand the various stages of the defect. Using Machine learning techniques, we could predict whether the defect will be back to submitter for clarification as need information state or the issue is fixed. In this paper we will discuss the machine learning techniques for predicting the defect back to tester for need information state and the method of accuracy in prediction.
Keywords: Machine Learning, Prediction, Defect, Maintenance, Performance.
Scope of the Article: Machine Learning