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IJSDR
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

Issue: March 2024

Volume 9 | Issue 3

Impact factor: 8.15

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Paper Title: PREDICTING SOFTWARE DEFECT COMPLEXITY USING BUG TRACKING
Authors Name: KORRAPATI BHUVANESWARA CHARI , VERPULA YASHWANTH KUMAR , REDDYPALLY SAIKAR , B SAVIT , K ANURANJANI
Unique Id: IJSDR2304184
Published In: Volume 8 Issue 4, April-2023
Abstract: Numerous free, open-source, and paid bug tracking programmes have been created or are constantly being created. As there are more problems with software projects every day, developers are beginning to use bug tracking tools to keep track of the bug reports. The industry need the criteria for choosing the best system tool from the assortment of system tools that are currently accessible in order to track and correct bugs and report them as they are fixed. While there are numerous bug tracking solutions that deliver the data via different resources, such as web interfaces, it is still a challenge to gather usable information from the huge and disorganised set of complaints. To handle the reviews of the available tools and offer a new improved tool for the bug tracking and reporting system, we attempt to present these thorough classification criteria. Assigning the bug to the developer for monitoring and resolving the progress of bug fixing through various graphical/charting facilities and status updates also helps in reporting the bugs that are discovered by that method. Additionally, it attempts to detect bugs for complexity measures and offers the reliability of bug prediction, enabling distribution of patches to consumers. Support Vector Machine (SVM), an established algorithm, and Convolution Neural Network (CNN), a suggested algorithm, will both be used in our research. The outcomes demonstrate that the proposed Convolution Neural Network (CNN) performs better than the current SVM.
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Cite Article: "PREDICTING SOFTWARE DEFECT COMPLEXITY USING BUG TRACKING", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 4, page no.1120 - 1124, April-2023, Available :http://www.ijsdr.org/papers/IJSDR2304184.pdf
Downloads: 000336256
Publication Details: Published Paper ID: IJSDR2304184
Registration ID:205244
Published In: Volume 8 Issue 4, April-2023
DOI (Digital Object Identifier):
Page No: 1120 - 1124
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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