  Migrating a monolith application into a microservices architecture can
benefit from automation methods, which speed up the migration and improve the
decomposition results. One of the current approaches that guide software
architects on the migration is to group monolith domain entities into
microservices, using the sequences of accesses of the monolith functionalities
to the domain entities. In this paper, we enrich the sequence of accesses
solution by applying code vectorization to the monolith, using the
\textit{Code2Vec} neural network model. We apply \textit{Code2Vec} to vectorize
the monolith functionalities. We propose two strategies to represent a
functionality, one by aggregating its call graph methods vectors, and the other
by extending the sequence of accesses approach with vectorization of the
accessed entities. To evaluate these strategies, we compare the proposed
strategies with the sequence of accesses strategy and an existing approach that
uses class vectorization. We run all these strategies over a large set of
codebases, and then compare the results of their decompositions in terms of
cohesion, coupling, and complexity.
