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인공지능대학
컴퓨터공학과
Journal papers
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Reduction of False Positives for Runtime Errors in C/C++ Software: A Comparative Study
Title
Reduction of False Positives for Runtime Errors in C/C++ Software: A Comparative Study
Authors
Park J.
;
Shin J.
;
Choi B.
Ewha Authors
최병주
;
박지현
SCOPUS Author ID
최병주
; 박지현
Issue Date
2023
Journal Title
Electronics (Switzerland)
ISSN
2079-9292
Citation
Electronics (Switzerland) vol. 12, no. 16
Keywords
deep learning
;
early defect detection
;
false positive rate
;
machine learning
;
static analysis
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Indexed
SCIE; SCOPUS
Document Type
Article
Abstract
In software development, early defect detection using static analysis can be performed without executing the source code. However, defects are detected on a non-execution basis, thus resulting in a higher ratio of false positives. Recently, studies have been conducted to effectively perform static analyses using machine learning (ML) and deep learning (DL) technologies. This study examines the techniques for detecting runtime errors used in existing static analysis tools and the causes and rates of false positives. It analyzes the latest static analysis technologies that apply machine learning/deep learning to decrease false positives and compares them with existing technologies in terms of effectiveness and performance. In addition, machine-learning/deep-learning-based defect detection techniques were implemented in experimental environments and real-world software to determine their effectiveness in real-world software. © 2023 by the authors.
DOI
10.3390/electronics12163518
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