  Trustworthiness and interpretability are inextricably linked concepts for
LLMs. The more interpretable an LLM is, the more trustworthy it becomes.
However, current techniques for interpreting LLMs when applied to code-related
tasks largely focus on accuracy measurements, measures of how models react to
change, or individual task performance instead of the fine-grained explanations
needed at prediction time for greater interpretability, and hence trust. To
improve upon this status quo, this paper introduces ASTrust, an
interpretability method for LLMs of code that generates explanations grounded
in the relationship between model confidence and syntactic structures of
programming languages. ASTrust explains generated code in the context of syntax
categories based on Abstract Syntax Trees and aids practitioners in
understanding model predictions at both local (individual code snippets) and
global (larger datasets of code) levels. By distributing and assigning model
confidence scores to well-known syntactic structures that exist within ASTs,
our approach moves beyond prior techniques that perform token-level confidence
mapping by offering a view of model confidence that directly aligns with
programming language concepts with which developers are familiar. To put
ASTrust into practice, we developed an automated visualization that illustrates
the aggregated model confidence scores superimposed on sequence, heat-map, and
graph-based visuals of syntactic structures from ASTs. We examine both the
practical benefit that ASTrust can provide through a data science study on 12
popular LLMs on a curated set of GitHub repos and the usefulness of ASTrust
through a human study.
