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Volume 13 | Issue 10 | Year 2026 | Article Id. IJECE-V13I10P108 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I10P108

A CNN-LSTM-based Hybrid Deep Learning Model Enhanced with PSO-GA Optimization for Accurate Code Clone Detection

Tanvi, Geetika

Received Revised Accepted Published
21 Aug 2026 15 Sep 2026 24 Sep 2026 28 Oct 2026

Citation :

Tanvi, Geetika, "A CNN-LSTM-based Hybrid Deep Learning Model Enhanced with PSO-GA Optimization for Accurate Code Clone Detection," International Journal of Electronics and Communication Engineering, vol. 13, no. 10, pp. 131-143, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I10P108

Abstract

Code clone analysis is needed when software repositories contain multiple implementations of the same or closely related functionality. Such duplication can make later maintenance harder because a change made in one fragment may not be reflected in another. This paper presents a hybrid CNN–LSTM framework in which PSO and GA are used to refine the feature representation before classification. The workflow consists of source-code preparation, feature construction, optimization, and clone classification. CNN is used to learn local patterns from the code representation, whereas LSTM models dependencies that extend across the sequence. The optimized representation is evaluated on BigCloneBench, Google Code Jam, and a Java repository. The reported experiments show that the combined model provides higher recall, accuracy, and precision than the comparison models. The findings indicate that optimization-assisted deep learning can provide a practical route toward more reliable clone analysis in software repositories.

Keywords

Clone detection accuracy, CNN-LSTM, Feature set optimization, Learning-based software engineering, PSO-GA.

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