A novel code representation for detecting Java code clones using high-level and abstract compiled code representations.

Gespeichert in:
Bibliographische Detailangaben
Titel: A novel code representation for detecting Java code clones using high-level and abstract compiled code representations.
Autoren: Quradaa, Fahmi H., Shahzad, Sara, Saeed, Rashad, Sufyan, Mubarak M.
Quelle: PLoS ONE; 5/10/2024, Vol. 19 Issue 5, p1-31, 31p
Schlagwörter: COMPUTER software quality control, SOURCE code, COMPUTER software development, MACHINE learning, SOOT
Abstract: In software development, it's common to reuse existing source code by copying and pasting, resulting in the proliferation of numerous code clones—similar or identical code fragments—that detrimentally affect software quality and maintainability. Although several techniques for code clone detection exist, many encounter challenges in effectively identifying semantic clones due to their inability to extract syntax and semantics information. Fewer techniques leverage low-level source code representations like bytecode or assembly for clone detection. This work introduces a novel code representation for identifying syntactic and semantic clones in Java source code. It integrates high-level features extracted from the Abstract Syntax Tree with low-level features derived from intermediate representations generated by static analysis tools, like the Soot framework. Leveraging this combined representation, fifteen machine-learning models are trained to effectively detect code clones. Evaluation on a large dataset demonstrates the models' efficacy in accurately identifying semantic clones. Among these classifiers, ensemble classifiers, such as the LightGBM classifier, exhibit exceptional accuracy. Linearly combining features enhances the effectiveness of the models compared to multiplication and distance combination techniques. The experimental findings indicate that the proposed method can outperform the current clone detection techniques in detecting semantic clones. [ABSTRACT FROM AUTHOR]
Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Datenbank: Complementary Index