An accurate estimation of the Levenshtein distance using metric trees and Manhattan distance

Abstract

This paper presents an original clone detection technique which is an accurate approximation of the Levenshtein distance. It uses groups of tokens extracted from source code called windowed-tokens. From these, frequency vectors are then constructed and compared with the Manhattan distance in a metric tree. The goal of this new technique is to provide a very high precision clone detection technique while keeping a high recall. Precision and recall measurement is done with respect to the Levenshtein distance. The testbench is a large scale open source software. The collected results proved the technique to be fast, simple, and accurate. Finally, this article presents further research opportunities.