Reduction of False Positives for Runtime Errors in C/C++ Software: A Comparative Study

Jihyun Park, Jaeyoung Shin, Byoungju Choi

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number3518
JournalElectronics (Switzerland)
Issue number16
StatePublished - Aug 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.


  • deep learning
  • early defect detection
  • false positive rate
  • machine learning
  • static analysis


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