Analyze the Performance of Software by Machine Learning Methods for Fault Prediction Techniques

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Different Phases of bug life cycle
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Published: May 17, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5s.6642
Keywords:
Fault, Machine Learning, Decision Tree, SVM, KNN, Ensemble Techniques, Software, Fault detection model

Main Article Content

Nikita Gupta
Department of Computer Science, RTU, Kota, Rajasthan, India
Ripu Ranjan Sinha
Department of Computer Science, RTU, Kota, Rajasthan, India
Ankur Goyal
Department of Computer Science, Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, Maharashtra, India
Neelam Sunda
Department of Computer Science, RTU, Kota, Rajasthan, India
Divya Sharma
Assistant Professor, Kanoria PG Mahila Mahavidyalaya, Jaipur, Rajasthan, India

Abstract

Trend of using the software in daily life is increasing day by day. Software system development is growing more difficult as these technologies are integrated into daily life. Therefore, creating highly effective software is a significant difficulty. The quality of any software system continues to be the most important element among all the required characteristics. Nearly one-third of the total cost of software development goes toward testing. Therefore, it is always advantageous to find a software bug early in the software development process because if it is not found early, it will drive up the cost of the software development. This type of issue is intended to be resolved via software fault prediction. There is always a need for a better and enhanced prediction model in order to forecast the fault before the real testing and so reduce the flaws in the time and expense of software projects. The various machine learning techniques for classifying software bugs are discussed in this paper.

Article Details

How to Cite
Gupta, N. ., Sinha, R. R. ., Goyal, A. ., Sunda, N. ., & Sharma, D. . (2023). Analyze the Performance of Software by Machine Learning Methods for Fault Prediction Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 178–187. https://doi.org/10.17762/ijritcc.v11i5s.6642
Issue
Vol. 11 No. 5s (2023): Special Issue on Advances in Computing Systems for Web-Based Machine Learning Applications
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Articles

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