  Language models for code such as CodeBERT offer the capability to learn
advanced source code representation, but their opacity poses barriers to
understanding of captured properties. Recent attention analysis studies provide
initial interpretability insights by focusing solely on attention weights
rather than considering the wider context modeling of Transformers. This study
aims to shed some light on the previously ignored factors of the attention
mechanism beyond the attention weights. We conduct an initial empirical study
analyzing both attention distributions and transformed representations in
CodeBERT. Across two programming languages, Java and Python, we find that the
scaled transformation norms of the input better capture syntactic structure
compared to attention weights alone. Our analysis reveals characterization of
how CodeBERT embeds syntactic code properties. The findings demonstrate the
importance of incorporating factors beyond just attention weights for
rigorously understanding neural code models. This lays the groundwork for
developing more interpretable models and effective uses of attention mechanisms
in program analysis.
