Semantic code clone detection using hybrid intermediate representations and BiLSTM networks.

ABSTRACT: Semantic code clone detection plays an essential role in software maintenance and quality assurance, as it helps uncover fragments of code that express the same logic even when their syntax has been altered or deliberately obfuscated. In this study, we propose a framework that combines hybrid representation learning with deep bidirectional LSTM networks. The model is applied to two intermediate forms of Java programs-Baf and Jimple-extracted through the Soot framework, which together provide both syntactic structure and semantic detail. This design allows the method to cope with difficult obfuscation strategies such as polymorphism and metamorphism. In our experiments, the framework showed strong and stable performance. Training accuracy reached about 98%, while validation accuracy stayed

SUBMITTER: Shahbaz Ismail M 

PROVIDER: S-EPMC12818651 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature