Machine Learning- Based Approach For Classifying the source Code Using Programming Keywords (Record no. 17234)

MARC details
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003 - CONTROL NUMBER IDENTIFIER
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005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220804111032.0
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fixed length control field 220730b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency AIKTC-KRRC
Transcribing agency AIKTC-KRRC
100 ## - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 17409
Author Ifham, Mohamed.
245 ## - TITLE STATEMENT
Title Machine Learning- Based Approach For Classifying the source Code Using Programming Keywords
250 ## - EDITION STATEMENT
Volume, Issue number Vol,18(1), March
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Hyderbad
Name of publisher, distributor, etc. IUP Publications
Year 2022
300 ## - PHYSICAL DESCRIPTION
Pagination 7-25p.
520 ## - SUMMARY, ETC.
Summary, etc. Implementation Phase is one of the most critical periods in software development. Developers build their source code or reuse old source code functionalities concerning the requirement of the system.Most developers spend more time searching and navigating old source codes than developing them.It is essential to have an efficient method to search source cod functionality within a short period.topic modeling of source code is an approach used to extract topic from source codes. many topic modeling approaches have been implemented using statistical techniques, which have many setbacks. Those results rely on non formal code elements such as identifier names, comments etc.Our novel approach is implemented using a machine learning algorithm to address these issues.the source code functionality results depend only on the algorithm or the syntax or the syntax of the source code.Three java project functionalities,such as prim number, fibonacci number,and selection sort were evaluated in this study. java parser library is used to derive the source code elements,and an algorithm is created to take the count matrix of the source code features. Then the detaset was fed to three models artificial Neural Network (ANN),Random Forest (RF),and Ensemble Approach. It was found that the Ensemble Approach showed a 96.7% accuracy by surpassing ANN and RF.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4619
Topical term or geographic name entry element EXTC Engineering
700 ## - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 17410
Co-Author Kumara, Btgs
773 0# - HOST ITEM ENTRY
International Standard Serial Number 0973-2896
Place, publisher, and date of publication Hyderabad IUP Publications
Title IUP journal of information technology
942 ## - ADDED ENTRY ELEMENTS (KOHA)
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Koha item type Articles Abstract Database
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    Dewey Decimal Classification     School of Engineering & Technology School of Engineering & Technology Archieval Section 30/07/2022   2022-1236 30/07/2022 30/07/2022 Articles Abstract Database
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