Deep Learning-Based Software Defect Prediction via Semantic Key Features of Source Code—Systematic Survey

Ahmed Abdu, Zhengjun Zhai (), Redhwan Algabri (), Hakim A. Abdo, Kotiba Hamad () and Mugahed A. Al-antari ()
Additional contact information
Ahmed Abdu: School of Software, Northwestern Polytechnical University, Xi’an 710072, China
Zhengjun Zhai: School of Software, Northwestern Polytechnical University, Xi’an 710072, China
Redhwan Algabri: School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Korea
Hakim A. Abdo: Department of Computer Science, Hodeidah University, Al-Hudaydah P.O. Box 3114, Yemen
Kotiba Hamad: School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon 16419, Korea
Mugahed A. Al-antari: Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Korea

Mathematics, 2022, vol. 10, issue 17, 1-26

Abstract: Software defect prediction (SDP) methodology could enhance software’s reliability through predicting any suspicious defects in its source code. However, developing defect prediction models is a difficult task, as has been demonstrated recently. Several research techniques have been proposed over time to predict source code defects. However, most of the previous studies focus on conventional feature extraction and modeling. Such traditional methodologies often fail to find the contextual information of the source code files, which is necessary for building reliable prediction deep learning models. Alternatively, the semantic feature strategies of defect prediction have recently evolved and developed. Such strategies could automatically extract the contextual information from the source code files and use them to directly predict the suspicious defects. In this study, a comprehensive survey is conducted to systematically show recent software defect prediction techniques based on the source code’s key features. The most recent studies on this topic are critically reviewed through analyzing the semantic feature methods based on the source codes, the domain’s critical problems and challenges are described, and the recent and current progress in this domain are discussed. Such a comprehensive survey could enable research communities to identify the current challenges and future research directions. An in-depth literature review of 283 articles on software defect prediction and related work was performed, of which 90 are referenced.

Keywords: software defect prediction (SDP); source code representation; deep learning; semantic key features (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/17/3120/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/17/3120/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:17:p:3120-:d:902398

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().