Accurate and Language Agnostic Code Clone Detection by Measuring Edit Distance of ANTLR Parse Tree

Abstract

In spite of significant research done in the past 3 decades introducing more than 250 clone detection tools/ techniques for finding the same language clones, there exists no single framework to detect and classify all 4 basic types of clones with great accuracy (precision and recall). In this paper, we propose an accurate and language agnostic technique to classify 4 types of clones. The method first generates an ANTLR parse tree for the input program file using freely available ANTLR grammar files then finds the edit distance between the two parse trees using the Levenshtein distance algorithm and converts the edit distance into similarity using. We obtained 100% precision and recall in detecting type 1 & 2 clone types and achieve 98.50 and 98.12 respectively for type 3 and 4 clone types for our datasets containing microprograms of C, CPP, and Java. This paper provides evidence that the Levenshtein distance on ANTLR parse tree is the good choice to build a complete and accurate software clone detector and act as proper validation tools to detect code plagiarism.