  As researchers and practitioners apply Machine Learning to increasingly more
software engineering problems, the approaches they use become more
sophisticated. A lot of modern approaches utilize internal code structure in
the form of an abstract syntax tree (AST) or its extensions: path-based
representation, complex graph combining AST with additional edges. Even though
the process of extracting ASTs from code can be done with different parsers,
the impact of choosing a parser on the final model quality remains unstudied.
Moreover, researchers often omit the exact details of extracting particular
code representations.
  In this work, we evaluate two models, namely Code2Seq and TreeLSTM, in the
method name prediction task backed by eight different parsers for the Java
language. To unify the process of data preparation with different parsers, we
develop SuperParser, a multi-language parser-agnostic library based on
PathMiner. SuperParser facilitates the end-to-end creation of datasets suitable
for training and evaluation of ML models that work with structural information
from source code. Our results demonstrate that trees built by different parsers
vary in their structure and content. We then analyze how this diversity affects
the models' quality and show that the quality gap between the most and least
suitable parsers for both models turns out to be significant. Finally, we
discuss other features of the parsers that researchers and practitioners should
take into account when selecting a parser along with the impact on the models'
quality.
  The code of SuperParser is publicly available at
https://doi.org/10.5281/zenodo.6366591. We also publish Java-norm, the dataset
we use to evaluate the models: https://doi.org/10.5281/zenodo.6366599.
