Source codes classification using a modified instruction count pass

Omar Darwish, Majdi Maabreh, Ola Karajeh, Belal Alsinglawi

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

1 Citation (Scopus)

Abstract

![CDATA[The vulnerability is a flaw in the system’s implementation which may result in severe consequences. The existence of these flaws should be detected and managed. There are several types of research which provide different solutions to detect these flaws through static analysis of the original source codes. Static analysis process has many disadvantages, some of them are; slower than compilation and produce high false positive rate. In this project, we introduce a prediction technique using the output of one of the LLVM passes; “InstCount”. A classifier was built based on the output of this pass on 500 source codes written in C and C++ languages with 88% of accuracy. A comparison between our classifier and Clang static analyzer showed that the classifier super performed to predict the existence of memory leak and Null pointers. The experiment also showed that this classifier could be applied or integrated with static analysis tools for more efficient results.]]
Original languageEnglish
Title of host publicationWeb, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 33rd International Conference on Advanced Information Networking and Applications (WAINA-2019), Matsue, Japan, 27-29 March 2019
PublisherSpringer Nature
Pages897-906
Number of pages10
ISBN (Print)9783030150341
DOIs
Publication statusPublished - 2019
EventInternational Conference on Advanced Information Networking and Applications -
Duration: 27 Mar 2019 → …

Publication series

Name
ISSN (Print)2194-5357

Conference

ConferenceInternational Conference on Advanced Information Networking and Applications
Period27/03/19 → …

Keywords

  • memory
  • programming languages (electronic computers)
  • source code (computer science)
  • vulnerability

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