IJSDR
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: COMPREHENSIVE SURVEY OF DIFFERENT MACHINE LEARNING ALGORITHMS USED FOR SOFTWARE DEFECT PREDICTION
Authors Name: SINDHUJHA CHAVA , SAI BHAVANA PAGADAVARAPU , SWARNA TEJASRI
Unique Id: IJSDR2304213
Published In: Volume 8 Issue 4, April-2023
Abstract: software failure prediction performs an important function in improving software high-quality and helps reduce the time and price of software program checking out. Machine gaining knowledge of makes a speciality of growing pc applications that teach themselves to develop and change while exposed to new records. A machine's capability to perform its job is based totally on preceding outcomes. Machine getting to know enhances the effectiveness of human getting to know, reveals new items or systems unknown to humans, and unearths vital statistics in a document. To do this, various machine gaining knowledge of strategies were used to put off useless, misguided information from the parish information. Software failure prediction is visible as a completely critical capability in software layout and lots extra attempt is required to solve this complicated problem of the use of metric and failure facts. Metrics are the connection among a numerical cost and the way it is applied in a application, so that they generally tend to predict failure. The essential reason of this overview paper is to apprehend the prevailing methods for predicting software program disasters. The results acquired show that the proposed technique is more green in terms of accuracy as compared to other methods, consisting of SVM, Naive Bayes and Decision Tree. The effects acquired by means of the proposed technique have values for the duration (min) of 3.24 minutes, accuracy (%) of ninety 69.8% and accuracy (%) of 0.21%.
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Cite Article: "COMPREHENSIVE SURVEY OF DIFFERENT MACHINE LEARNING ALGORITHMS USED FOR SOFTWARE DEFECT PREDICTION", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 4, page no.1312 - 1319, April-2023, Available :http://www.ijsdr.org/papers/IJSDR2304213.pdf
Downloads: 000336257
Publication Details: Published Paper ID: IJSDR2304213
Registration ID:205378
Published In: Volume 8 Issue 4, April-2023
DOI (Digital Object Identifier):
Page No: 1312 - 1319
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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