Algorithm to derive shortest edit script using Levenshtein distance algorithm

TL;DR

This paper introduces an algorithm that leverages Levenshtein distance to generate the shortest edit script for string similarity, providing a new approach to compute string transformations efficiently.

Contribution

It presents a novel method that uses string similarity to derive the shortest edit script, differing from traditional LCS-based algorithms.

Findings

The algorithm effectively computes the shortest edit script.

It utilizes Levenshtein distance to determine string similarity.

The method produces instructions for Insert, Delete, and Substitute operations.

Abstract

String similarity, longest common subsequence and shortest edit scripts are the triplets of problem that related to each other. There are different algorithms exist to generate edit script by solving longest common subsequence problem. This paper proposes an algorithm that uses string similarity problem to generate shortest edit script. For this we use the famous Levenshtein distance algorithm, which computes a numerical value that represents similarity between the strings from 0 to n, where n is the length of longest input string, and produce the shortest edit script which contains instructions of Insert, Delete and Substitute.