Software Defect Estimation using Machine Learning Algorithms
Revoori Veeharika Reddy1, Nagella Kedharnath2, Mandi Akif Hussain3, S. Vidya4
1Mandi Akif Hussain*, Pursuing, Graduation in Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil (Tamil Nadu), India.
2Revoori Veeharika Reddy, Pursuing, Graduation in Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil (Tamil Nadu), India.
3Nagella Kedharnath, Pursuing, Graduation in Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil (Tamil Nadu), India.
4S. Vidya, Associate Professor, Department Computer Science, Kalasalingam Academy of Research and Education, Krishnankoil (Tamil Nadu), India.
Manuscript received on May 17, 2021. | Revised Manuscript received on May 20, 2021. | Manuscript published on May 30, 2021. | PP: 203-208 | Volume-10 Issue-1, May 2021. | Retrieval Number: 100.1/ijrte.A58980510121 | DOI: 10.35940/ijrte.A5898.0510121
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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 Engineering is a branch of computer science that enables tight communication between system software and training it as per the requirement of the user. We have selected seven distinct algorithms from machine learning techniques and are going to test them using the data sets acquired for NASA public promise repositories. The results of our project enable the users of this software to bag up the defects are selecting the most efficient of given algorithms in doing their further respective tasks, resulting in effective results.
Keywords: Software Quality Metrics, Software Defect Prediction, Software Fault Prediction, Machine Learning Algorithms.