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

M Shahbaz Ismail¹, Sara Shahzad¹, Fahmi H Quradaa²

¹Department of Computer Science, University of Peshawar, Peshawar, Pakistan.

|January 20, 2026

Summary

This summary is machine-generated.

This study introduces a novel framework for semantic code clone detection using hybrid representations and deep bidirectional LSTM networks. The approach achieves high accuracy, outperforming other models and demonstrating practical applicability in software maintenance.

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Area of Science:

Computer Science
Software Engineering

Background:

Semantic code clone detection is crucial for software maintenance and quality assurance.
Existing methods struggle with obfuscated code, including polymorphism and metamorphism.

Purpose of the Study:

To propose a robust framework for semantic code clone detection.
To address challenges posed by code obfuscation techniques.

Main Methods:

Utilizing hybrid representation learning with deep bidirectional Long Short-Term Memory (BiLSTM) networks.
Applying the model to Baf and Jimple intermediate representations extracted via the Soot framework.
Incorporating attention mechanisms and multiple intermediate representations.

Main Results:

Achieved high training (98%) and validation (95%) accuracy with good generalization.
BiLSTM models consistently outperformed other recurrent models.
Demonstrated state-of-the-art performance on the BigCloneBench dataset, with recall and F1-scores up to 97%.

Conclusions:

Hybrid intermediate representations are effective for semantic clone detection.
The proposed BiLSTM framework offers a practical solution for identifying code clones, even under obfuscation.
Future research could explore transformer-based models for enhanced performance.