Code clones are semantically similar code fragments pairs that are
syntactically similar or different. Detection of code clones can help to reduce
the cost of software maintenance and prevent bugs. Numerous approaches of
detecting code clones have been proposed previously, but most of them focus on
detecting syntactic clones and do not work well on semantic clones with
different syntactic features. To detect semantic clones, researchers have tried
to adopt deep learning for code clone detection to automatically learn latent
semantic features from data. Especially, to leverage grammar information,
several approaches used abstract syntax trees (AST) as input and achieved
significant progress on code clone benchmarks in various programming languages.
However, these AST-based approaches still can not fully leverage the structural
information of code fragments, especially semantic information such as control
flow and data flow. To leverage control and data flow information, in this
paper, we build a graph representation of programs called flow-augmented
abstract syntax tree (FA-AST). We construct FA-AST by augmenting original ASTs
with explicit control and data flow edges. Then we apply two different types of
graph neural networks (GNN) on FA-AST to measure the similarity of code pairs.
As far as we have concerned, we are the first to apply graph neural networks on
the domain of code clone detection.
We apply our FA-AST and graph neural networks on two Java datasets: Google
Code Jam and BigCloneBench. Our approach outperforms the state-of-the-art
approaches on both Google Code Jam and BigCloneBench tasks.

本文提出了一种基于图神经网络 (GNN) 的程序控制流和数据流表示方法 FA-AST (flow-augmented abstract syntax tree)，并将其应用于代码克隆检测，相比基于抽象语法树 (AST) 的方法，在 Google Code Jam 和 BigCloneBench 数据集上均取得了更好的效果。