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The software defect prediction technique yields result that development teams may examine and further contribute to industrial results. It finds all the problematic code portions, helps software developers uncover bugs, and helps them design their testing methods with the help of the model prediction. It is essential to know what percentage of categories yield the accurate forecast for early detection. Moreover, software-defected data sets are supported and at least partially recognized due to their huge dimension. Random forests (RF) and Artificial Neural Networks (ANN) are the machine learning techniques utilized in this research. The forecast for defects is created using historical data. The outcomes showed that the artificial neural network classifier performed better than the random forest classifier.
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Copyright © 2024, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: B.J.Priyanka, bjpriyanka@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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