Analysis on Detecting a Bug in a Software using Machine Learning
Rashmi P1, Prashanth Kambli2
1Rashmi P, Department of CSE, MSRIT, Bangalore, India.
2Prashanth Kambli Assistant professor, Department of MSRIT, IS&E, Bangalore, India.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 1195-1199 | Volume-9 Issue-2, July 2020. | Retrieval Number: B4119079220/2020©BEIESP | DOI: 10.35940/ijrte.B4119.079220
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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 today’s scenario, it is very essential in the development phase of a software, predicting a bug and to obtain a successful software. This can be achieved only through predicting some of the faults in the earlier phase itself such that, it can lead to have a reliable, efficient and a quality software. The challenging task here is to have a well sophisticated model that can predict the bug leading to a cost-effective software. In order to achieve this, few machine learning algorithms are used that produce accuracy with trained and test datasets. A variety of machine learning methodologies have been developed to learn and detect a bug in a software. In this paper, we perform the analysis on detecting a bug in a software using machine learning methods which is very much useful for Software Industries. It summarizes the existing work on detecting a bug in a software by providing the information about various methods involved in bug prediction and points out at the accuracy obtained by the existing methods, advantages, and the drawbacks while working with bug prediction.
Keywords: Software Development, Software Bug Prediction, Quality Software, Machine Learning, Accuracy.