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Research Article | Open Access | Online First

CloneTracker: Advanced Dual-Layered Approach for Precise Detection of Clones in Java Applications

Software School, Tsinghua University, Beijing 100084, China
Department of Computer Science, Bilkent University, Ankara 06800, Türkiye
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Abstract

In the fast-paced world of software development, conventional code clone detection methods are becoming inadequate, frequently struggling to identify complex clones. This gap significantly endangers software quality and security, emphasizing the need for advanced clone detection approach. In response, our CloneTracker introduces an advanced, dual-layered solution to identify all types of clones. CloneTracker utilizes a dual-layer detection strategy based on function-level and chunk-level analysis for enhanced accuracy. Specifically, it prioritizes function-based signature detection, followed by chunk-based detection, requiring at least 50% similarity and three matching chunks from the same file for clone identification. The dual-level indexing and synergistic abstraction distinguish our approach, surpassing traditional methods in efficiency and effectiveness. A comprehensive evaluation against well-known clone detection techniques across multiple reliable datasets revealed CloneTracker’s peerless precision average of 98%, recall ranging from 99.0% to 100%, accuracy ranging from 97.5% to 99.0%, and efficiency of 1.14 to 1.29 s, with these results reflecting averages over function-based and file-based assessment. Its superior efficacy reaffirms the approach’s importance in modern software development, setting a new standard for clone detection and paving the way for future advancements. Beyond mere detection, CloneTracker also precisely identifies changes in cloned code, enhancing code quality and maintenance practices in the modern software development paradigm.

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Cite this article:
Glani Y, Luo P, Shah SA, et al. CloneTracker: Advanced Dual-Layered Approach for Precise Detection of Clones in Java Applications. Tsinghua Science and Technology, 2026, https://doi.org/10.26599/TST.2025.9010035

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Received: 05 May 2024
Revised: 01 September 2024
Accepted: 10 March 2025
Published: 02 July 2026
© The author(s) 2026.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).