  Recently program learning techniques have been proposed to process source
code based on syntactical structures (e.g., Abstract Syntax Trees) and/or
semantic information (e.g., Dependency Graphs). Although graphs may be better
at capturing various viewpoints of code semantics than trees, constructing
graph inputs from code needs static code semantic analysis that may not be
accurate and introduces noise during learning. Although syntax trees are
precisely defined according to the language grammar and easier to construct and
process than graphs, previous tree-based learning techniques have not been able
to learn semantic information from trees to achieve better accuracy than
graph-based techniques. We propose a new learning technique, named TreeCaps, by
fusing together capsule networks with tree-based convolutional neural networks,
to achieve learning accuracy higher than existing graph-based techniques while
it is based only on trees. TreeCaps introduces novel variable-to-static routing
algorithms into the capsule networks to compensate for the loss of previous
routing algorithms. Aside from accuracy, we also find that TreeCaps is the most
robust to withstand those semantic-preserving program transformations that
change code syntax without modifying the semantics. Evaluated on a large number
of Java and C/C++ programs, TreeCaps models outperform prior deep learning
models of program source code, in terms of both accuracy and robustness for
program comprehension tasks such as code functionality classification and
function name prediction
