Plagiarism detection in SQL student assignments

Nikolai Scerbakov; Alexander Schukin; Oleg Sabinin

Plagiarism detection in SQL student assignments

Nikolai Scerbakov, Alexander Schukin, Oleg Sabinin

Institute of Interactive Systems and Data Science (7060)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

In this paper we present an original method of detecting similarity between SQL fragments. The method is based on identifying so-called "SQL lexemes" - persistent elements of an SQL statement, and "SQL variables" - easily modifiable elements of SQL statements. Thus, any SQL statements can be replaced with a so-called token - sequence of SQL lexemes and SQL variables. Distance between SQL tokens can be calculated using such a well-known algorithm as Levenshtein Metric. Small values of Levenshtein distance between tokens detect such SQL statements that were built by modifications of others.
We also present first practical results of actual application of the algorithm, and discuss further developments of the method.

Original language	English
Title of host publication	Proceedings of 20th International Conference on Interactive Collaborative Learning
Pages	321-326
Number of pages	6
Publication status	Published - 2017

ASJC Scopus subject areas

Computer Science(all)

Cite this

@inproceedings{f2bbecb8b0e74949bbf8208da1bf9e0c,

title = "Plagiarism detection in SQL student assignments",

abstract = "In this paper we present an original method of detecting similarity between SQL fragments. The method is based on identifying so-called {"}SQL lexemes{"} - persistent elements of an SQL statement, and {"}SQL variables{"} - easily modifiable elements of SQL statements. Thus, any SQL statements can be replaced with a so-called token - sequence of SQL lexemes and SQL variables. Distance between SQL tokens can be calculated using such a well-known algorithm as Levenshtein Metric. Small values of Levenshtein distance between tokens detect such SQL statements that were built by modifications of others.We also present first practical results of actual application of the algorithm, and discuss further developments of the method.",

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AU - Scerbakov, Nikolai

AU - Schukin, Alexander

AU - Sabinin, Oleg

PY - 2017

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N2 - In this paper we present an original method of detecting similarity between SQL fragments. The method is based on identifying so-called "SQL lexemes" - persistent elements of an SQL statement, and "SQL variables" - easily modifiable elements of SQL statements. Thus, any SQL statements can be replaced with a so-called token - sequence of SQL lexemes and SQL variables. Distance between SQL tokens can be calculated using such a well-known algorithm as Levenshtein Metric. Small values of Levenshtein distance between tokens detect such SQL statements that were built by modifications of others.We also present first practical results of actual application of the algorithm, and discuss further developments of the method.

AB - In this paper we present an original method of detecting similarity between SQL fragments. The method is based on identifying so-called "SQL lexemes" - persistent elements of an SQL statement, and "SQL variables" - easily modifiable elements of SQL statements. Thus, any SQL statements can be replaced with a so-called token - sequence of SQL lexemes and SQL variables. Distance between SQL tokens can be calculated using such a well-known algorithm as Levenshtein Metric. Small values of Levenshtein distance between tokens detect such SQL statements that were built by modifications of others.We also present first practical results of actual application of the algorithm, and discuss further developments of the method.

M3 - Conference paper

SP - 321

EP - 326

BT - Proceedings of 20th International Conference on Interactive Collaborative Learning

ER -