  Coding practices are increasingly used by software companies. Their use
promotes consistency, readability, and maintainability, which contribute to
software quality. Coding practices were initially enforced by general-purpose
linters, but companies now tend to design and adopt their own company-specific
practices. However, these company-specific practices are often not automated,
making it challenging to ensure they are shared and used by developers.
Converting these practices into linter rules is a complex task that requires
extensive static analysis and language engineering expertise. In this paper, we
seek to answer the following question: can coding practices be learned
automatically from examples manually tagged by developers? We conduct a
feasibility study using CodeBERT, a state-of-the-art machine learning approach,
to learn linter rules. Our results show that, although the resulting
classifiers reach high precision and recall scores when evaluated on balanced
synthetic datasets, their application on real-world, unbalanced codebases,
while maintaining excellent recall, suffers from a severe drop in precision
that hinders their usability.
