Authors:
Yuanyuan Li
;
Yu Sheng
;
Lei Xiao
and
Fu Wang
Affiliation:
Central South University, China
Keyword(s):
Similarity Detection, Structure-Metric, GST Algorithm, Sub-Graph Isomorphism.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Computer-Supported Education
;
e-Learning
;
e-Learning in Engineering Education
;
e-Learning Platforms
;
Enterprise Information Systems
;
Information Technologies Supporting Learning
;
Intelligent Tutoring Systems
;
Learning/Teaching Methodologies and Assessment
;
Simulation and Modeling
;
Simulation Tools and Platforms
;
Ubiquitous Learning
;
Virtual Labs and Virtual Classrooms
Abstract:
Code similarity detection has been studied for several decades, which are prevailing categorized into attributecounting
and structure-metric. Due to the one fold validity of attribute-counting for full replication, mature
systems usually use the GST string matching algorithm to detect code structure. However, the accuracy of
GST is vulnerable to interference in code similarity detection. This paper presents a code similarity detection
method combining string matching and sub-graph isomorphism. The similarity is calculated with the GST
algorithm. Then according to the similarity, the system determines whether further processing with the sub-graph
iIsomorphism algorithm is required. Extensive experimental results illustrate that our method significantly
enhances the efficiency of string matching as well as the accuracy of code similarity detecting.