Semantic and traditional feature fusion for software defect prediction using hybrid deep learning model.

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Bibliographic Details
Title: Semantic and traditional feature fusion for software defect prediction using hybrid deep learning model.
Authors: Abdu A; School of Software, Northwestern Polytechnical University, Xi'an, 710072, China., Zhai Z; School of Software, Northwestern Polytechnical University, Xi'an, 710072, China. zhaizjun@nwpu.edu.cn.; School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China. zhaizjun@nwpu.edu.cn., Abdo HA; School of Computer Science, Dr.Babasaheb Ambedkar Marathwada University, Aurangabad, India., Algabri R; Research Institute of Engineering and Technology, Hanyang University, Ansan, 15588, Korea. redhwan@hanyang.ac.kr., Al-Masni MA; Department of Artificial Intelligence and Data Science, College of Software and Convergence Technology, Sejong University, Seoul, 05006, Republic of Korea., Muhammad MS; Department of AI and Robotics, Sejong University, Seoul, 05006, South Korea., Gu YH; Department of Artificial Intelligence and Data Science, College of Software and Convergence Technology, Sejong University, Seoul, 05006, Republic of Korea. yhgu@sejong.ac.kr.
Source: Scientific reports [Sci Rep] 2024 Jul 01; Vol. 14 (1), pp. 14771. Date of Electronic Publication: 2024 Jul 01.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE; PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2045-2322
DOI:10.1038/s41598-024-65639-4