Go-clone: graph-embedding based clone detector for Golang

Abstract

Golang (short for Go programming language) is a fast and compiled language, which has been increasingly used in industry due to its excellent performance on concurrent programming. Golang redefines concurrent programming grammar, making it a challenge for traditional clone detection tools and techniques. However, there exist few tools for detecting duplicates or copy-paste related bugs in Golang. Therefore, an effective and efficient code clone detector on Golang is especially needed. In this paper, we present Go-Clone, a learning-based clone detector for Golang. Go-Clone contains two modules -- the training module and the user interaction module. In the training module, firstly we parse Golang source code into llvm IR (Intermediate Representation). Secondly, we calculate LSFG (labeled semantic flow graph) for each program function automatically. Go-Clone trains a deep neural network model to encode LSFGs for similarity classification. In the user interaction module, users can choose one or more Golang projects. Go-Clone identifies and presents a list of function pairs, which are most likely clone code for user inspection. To evaluate Go-Clone's performance, we collect 6,110 commit versions from 48 Github projects to construct a Golang clone detection data set. Go-Clone can reach the value of AUC (Area Under Curve) and ACC (Accuracy) for 89.61% and 83.80% in clone detection. By testing several groups of unfamiliar data, we also demonstrates the generility of Go-Clone. The address of the abstract demo video: https://youtu.be/o5DogtYGbeo