Additional Detection of Clones Using Locally Sensitive Hashing




language-independent incremental repeat detector, locally sensitive hashing, incremental approach, incremental step, experiment, hash segment, hash function, clone index, shingles, MinHashing, shingling


Today, there are many methods for detecting blocks with repetitions and redundancy in the program code. But mostly they turn out to be dependent on the programming language in which the software is developed and try to detect complex types of repeating blocks. Therefore, the goal of the research was to develop a language-independent repetition detector and expand its capabilities. In the development and operation of the language-independent incremental repeater detector, it was decided to conduct experiments for five open source systems for evaluation using the industrial detector SIG (Software Improvement Group), including the use of a tool syntactic analysis. But there was the question of extending the algorithm for additional detection of duplication and redundancy in the code, which was proposed by Hammel, and how improvements can be made to achieve independence from the programming language. Particular attention was paid to the empirical results presented in the original study, as their effectiveness is questionable. The main parameters that were considered when creating the index for LIIRD (Language-independent incremental repeat detector) and its expansion of the LSH (locally sensitive hashing): measuring time, memory and creating an incremental step. Based on the results of experiments conducted by the authors of Hammel’s work, there was a motivation to develop an extended approach. The idea of this approach is that according to the original study, the operation of calculating the entire block index with repeats and redundancy from scratch is very time consuming. Therefore, it is proposed to use LSH to obtain an effective assessment of the similarity of software project files.


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Author Biography

Nataliia I. Pravorska, Khmelnitsky National University, Ukraine

Nataliia I. Pravorska has the degree of Candidate of Pedagogical Sciences 2005, PhD in Pedagogy (theory and methods of informatics (computer science)) 2011, MS of Software Engineering 2021. She is a associate professor of the Department of Software Engineering at Khmelnytskyi National University (2004–). Research interests: C+++⁣+ and Java programming, object-oriented programming, development of software products based on mathematical models, Internet of Things. Educational activity. Teaches disciplines: Applied information systems, Software design, Object-oriented programming, Basics of team software development, Java programming technologies, Software systems development methodologies and technologies.


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How to Cite

Pravorska NI. Additional Detection of Clones Using Locally Sensitive Hashing. JCSANDM [Internet]. 2023 May 18 [cited 2023 Dec. 4];12(03):367–388. Available from:



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