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.

The rapid evolution of software development has accentuated the deficiencies of prevailing code clone detection techniques. As modern applications become more complex, traditional cloning tools often struggle to detect general and large-gap clones that undergo regular modification. Such challenges pose threats to software integrity, emphasizing the critical need for improved code cloning techniques. Observing the prevailing gap, we propose an innovative code clone dive (CCDive) code cloning technique, which is designed to detect an extensive range of clones, from direct clones to the often challenging large-gap clones, thoroughly covering different categories, such as very strongly Type-III, strongly Type-III, and moderate Type-III clones. In CCDive, the fusion of a level-by-level abstraction and an innovative similarity matching algorithm ensures the recognition of clones even when nearly half the original code in the chunk has been modified. Furthermore, by integrating the Smith–Waterman local sequence alignment, the capability of CCDive to spot exact code transformation locations can be enhanced. In a comprehensive evaluation, CCDive was compared with well-known code cloning techniques. The efficacy of CCDive was measured using precision, recall, F1-score, accuracy, and efficiency. CCDive consistently surpassed other techniques in the precision, recall, F1-score, and accuracy metrics for both file-based and function-based clone detection. The robust performance of CCDive emphasizes its effectiveness, reliability, accuracy, and efficiency, making it well-suited for practical applications in the real world.