{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:30:41Z","timestamp":1760059841300,"version":"build-2065373602"},"reference-count":62,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004425","name":"Asian Development Bank","doi-asserted-by":"publisher","award":["CRG-R2-SB-1"],"award-info":[{"award-number":["CRG-R2-SB-1"]}],"id":[{"id":"10.13039\/100004425","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Code duplication, commonly referred to as code cloning, is not inherent in software systems but arises due to various factors, such as time constraints in meeting project deadlines. These duplications, or \u201ccode clones\u201d, complicate the program structure and increase maintenance costs. Code clones are categorized into four types: Type-1, Type-2, Type-3, and Type-4. This study aims to address the adverse effects of code clones by introducing LeONet, a hybrid Deep Learning approach that enhances the detection of code clones in software systems. The hybrid approach, LeONet, combines LeNet-5 with Oreo\u2019s Siamese architecture. We extracted clone method pairs from the BigCloneBench Java repository. Feature extraction was performed using Abstract Syntax Trees, which are scalable and accurately represent the syntactic structure of the source code. The performance of LeONet was compared against other classifiers including ANN, LeNet-5, Oreo\u2019s Siamese, LightGBM, XGBoost, and Decision Tree. LeONet demonstrated superior performance among the classifiers tested, achieving the highest F1 score of 98.12%. It also compared favorably against state-of-the-art approaches, indicating its effectiveness in code clone detection. The results validate the effectiveness of LeONet in detecting code clones, outperforming existing classifiers and competing closely with advanced methods. This study underscores the potential of hybrid deep learning models and feature extraction techniques in improving the accuracy of code clone detection, providing a promising direction for future research in this area.<\/jats:p>","DOI":"10.3390\/bdcc9070187","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T11:52:58Z","timestamp":1752580378000},"page":"187","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LeONet: A Hybrid Deep Learning Approach for High-Precision Code Clone Detection Using Abstract Syntax Tree Features"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5290-8367","authenticated-orcid":false,"given":"Thanoshan","family":"Vijayanandan","sequence":"first","affiliation":[{"name":"Center for Nano Device Fabrication and Characterization (CNFC), Faculty of Technology, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri Lanka"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0265-2198","authenticated-orcid":false,"given":"Kuhaneswaran","family":"Banujan","sequence":"additional","affiliation":[{"name":"Faculty of Science and Engineering, Southern Cross University, Lismore, NSW 2480, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7648-9784","authenticated-orcid":false,"given":"Ashan","family":"Induranga","sequence":"additional","affiliation":[{"name":"Center for Nano Device Fabrication and Characterization (CNFC), Faculty of Technology, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri Lanka"},{"name":"Department of Engineering Technology, Faculty of Technology, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri Lanka"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3941-2275","authenticated-orcid":false,"given":"Banage T. G. S.","family":"Kumara","sequence":"additional","affiliation":[{"name":"Center for Nano Device Fabrication and Characterization (CNFC), Faculty of Technology, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri Lanka"},{"name":"Department of Data Science, Faculty of Computing, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri Lanka"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7183-406X","authenticated-orcid":false,"given":"Kaveenga","family":"Koswattage","sequence":"additional","affiliation":[{"name":"Center for Nano Device Fabrication and Characterization (CNFC), Faculty of Technology, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri Lanka"},{"name":"Department of Engineering Technology, Faculty of Technology, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri Lanka"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"ref_1","unstructured":"Kim, M., Bergman, L., Lau, T., and Notkin, D. 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