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Study of Software Defect Prediction using Forward Pass RNN with Hyperbolic Tangent Function

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Study of Software Defect Prediction using Forward Pass RNN with Hyperbolic Tangent Function


Swati Rai | Dr. Kirti Jain



Swati Rai | Dr. Kirti Jain "Study of Software Defect Prediction using Forward Pass RNN with Hyperbolic Tangent Function" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6, December 2023, pp.268-273, URL: https://www.ijtsrd.com/papers/ijtsrd60159.pdf

For the IT sector and software specialists, software failure prediction and proneness have long been seen as crucial issues. Conventional methods need prior knowledge of errors or malfunctioning modules in order to identify software flaws inside an application. By using machine learning approaches, automated software fault recovery models allow the program to substantially forecast and recover from software problems. This feature helps the program operate more efficiently and lowers errors, time, and expense. Using machine learning methods, a software fault prediction development model was presented, which might allow the program to continue working on its intended mission. Additionally, we assessed the model's performance using a variety of optimization assessment benchmarks, including accuracy, f1-measure, precision, recall, and specificity. Convolutional neural networks and its hyperbolic tangent functions are the basis of the deep learning prediction model FPRNN-HTF (Forward Pass RNN with Hyperbolic Tangent Function) technique. The assessment procedure demonstrated the high accuracy rate and effective application of CNN algorithms. Moreover, a comparative measure is used to evaluate the suggested prediction model against other methodologies. The gathered data demonstrated the superior performance of the FPRNN-HTF technique.

FPRNN-HTF (Forward Pass RNN with Hyperbolic Tangent Function), precision, recall, specificity, F1-measure, and accuracy


IJTSRD60159
Volume-7 | Issue-6, December 2023
268-273
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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