  Program similarity is a fundamental concept, central to the solution of
software engineering tasks such as software plagiarism, clone identification,
code refactoring and code search. Accurate similarity estimation between
programs requires an in-depth understanding of their structure, semantics and
flow. A control flow graph (CFG), is a graphical representation of a program
which captures its logical control flow and hence its semantics. A common
approach is to estimate program similarity by analysing CFGs using graph
similarity measures, e.g. graph edit distance (GED). However, graph edit
distance is an NP-hard problem and computationally expensive, making the
application of graph similarity techniques to complex software programs
impractical. This study intends to examine the effectiveness of graph neural
networks to estimate program similarity, by analysing the associated control
flow graphs. We introduce funcGNN, which is a graph neural network trained on
labeled CFG pairs to predict the GED between unseen program pairs by utilizing
an effective embedding vector. To our knowledge, this is the first time graph
neural networks have been applied on labeled CFGs for estimating the similarity
between high-level language programs. Results: We demonstrate the effectiveness
of funcGNN to estimate the GED between programs and our experimental analysis
demonstrates how it achieves a lower error rate (0.00194), with faster (23
times faster than the quickest traditional GED approximation method) and better
scalability compared with the state of the art methods. funcGNN posses the
inductive learning ability to infer program structure and generalise to unseen
programs. The graph embedding of a program proposed by our methodology could be
applied to several related software engineering problems (such as code
plagiarism and clone identification) thus opening multiple research directions.
