Challenging Machine Learning-Based Clone Detectors via Semantic-Preserving Code Transformations
Author:
Affiliation:
1. University of Science and Technology of China, Hefei, Anhui, China
2. Baidu Security, Beijing, China
3. University of Newcastle, Callaghan, NSW, Australia
4. University of New South Wales, Sydney, NSW, Australia
Funder
National Natural Science Foundation of China
Basic Research Program of Jiangsu Province
CAS Pioneer Hundred Talents Program of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Software
Link
http://xplorestaging.ieee.org/ielx7/32/10124809/10028657.pdf?arnumber=10028657
Reference88 articles.
1. Comparison and evaluation of code clone detection techniques and tools: A qualitative approach
2. Convolutional Neural Networks over Tree Structures for Programming Language Processing
3. CCGraph: a PDG-based code clone detector with approximate graph matching;zou;IEEE/ACM Int Conference on Automated Software Engineering (ASE),2020
4. Modular Tree Network for Source Code Representation Learning
5. Functional code clone detection with syntax and semantics fusion learning
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