Topical: Learning Repository Embeddings from Source Code using Attention. (arXiv:2208.09495v1 [cs.SE])

Machine learning on source code (MLOnCode) promises to transform how software
is delivered. By mining the context and relationship between software
artefacts, MLOnCode augments the software developers capabilities with code
auto-generation, code recommendation, code auto-tagging and other data-driven
enhancements. For many of these tasks a script level representation of code is
sufficient, however, in many cases a repository level representation that takes
into account various dependencies and repository structure is imperative, for
example, auto-tagging repositories with topics or auto-documentation of
repository code etc. Existing methods for computing repository level
representations suffer from (a) reliance on natural language documentation of
code (for example, README files) (b) naive aggregation of method/script-level
representation, for example, by concatenation or averaging. This paper
introduces Topical a deep neural network to generate repository level
embeddings of publicly available GitHub code repositories directly from source
code. Topical incorporates an attention mechanism that projects the source
code, the full dependency graph and the script level textual information into a
dense repository-level representation. To compute the repository-level
representations, Topical is trained to predict the topics associated with a
repository, on a dataset of publicly available GitHub repositories that were
crawled along with their ground truth topic tags. Our experiments show that the
embeddings computed by Topical are able to outperform multiple baselines,
including baselines that naively combine the method-level representations
through averaging or concatenation at the task of repository auto-tagging.



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