Method of Searching for Clones of the Program Code in Binary Executive Files

The modern trend of increasing labor productivity and business process efficiency entails the optimization of software development processes through the use of generative artificial intelligence models trained on various code bases and manual copying of code fragments. Given the growing number of registered vulnerabilities, methods for detecting clones of the software code are needed. A method for assessing the similarity of fragments of the program code of binary executable files, which is based on the representation of the code in the form of an FA-AAST tree and the apparatus of graph neural networks, is proposed. The results obtained during testing on open and closed source software demonstrate the correctness of the proposed method and higher accuracy compared to the solutions considered.

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Publication Details

Journal
Automatic Control and Computer Sciences
Published
2024-12-01
DOI
https://doi.org/10.3103/s0146411624700913
Citations
1
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
1.05
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article

Method of Searching for Clones of the Program Code in Binary Executive Files

A. V. Bulat, N. A. Gribkov, E. V. Zavadskii
1 citations
Automatic Control and Computer Sciences
Scientific Computing and Data Management
1.05
article

Method of Searching for Clones of the Program Code in Binary Executive Files

A. V. Bulat, N. A. Gribkov, E. V. Zavadskii
article en
1 citations

Abstract

The modern trend of increasing labor productivity and business process efficiency entails the optimization of software development processes through the use of generative artificial intelligence models trained on various code bases and manual copying of code fragments. Given the growing number of registered vulnerabilities, methods for detecting clones of the software code are needed. A method for assessing the similarity of fragments of the program code of binary executable files, which is based on the representation of the code in the form of an FA-AAST tree and the apparatus of graph neural networks, is proposed. The results obtained during testing on open and closed source software demonstrate the correctness of the proposed method and higher accuracy compared to the solutions considered.

Automatic Control and Computer SciencesVol. 58(8)
Peter the Great St. Petersburg Polytechnic University (RU)
Openalex Percentile: Top 11%
Scientific Computing and Data Management
1.05
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