  Recently, pretrained language models have shown state-of-the-art performance
on the vulnerability detection task. These models are pretrained on a large
corpus of source code, then fine-tuned on a smaller supervised vulnerability
dataset. Due to the different training objectives and the performance of the
models, it is interesting to consider whether the models have learned the
semantics of code relevant to vulnerability detection, namely bug semantics,
and if so, how the alignment to bug semantics relates to model performance. In
this paper, we analyze the models using three distinct methods:
interpretability tools, attention analysis, and interaction matrix analysis. We
compare the models' influential feature sets with the bug semantic features
which define the causes of bugs, including buggy paths and Potentially
Vulnerable Statements (PVS). We find that (1) better-performing models also
aligned better with PVS, (2) the models failed to align strongly to PVS, and
(3) the models failed to align at all to buggy paths. Based on our analysis, we
developed two annotation methods which highlight the bug semantics inside the
model's inputs. We evaluated our approach on four distinct transformer models
and four vulnerability datasets and found that our annotations improved the
models' performance in the majority of settings - 11 out of 16, with up to 9.57
points improvement in F1 score compared to conventional fine-tuning. We further
found that with our annotations, the models aligned up to 232% better to
potentially vulnerable statements. Our findings indicate that it is helpful to
provide the model with information of the bug semantics, that the model can
attend to it, and motivate future work in learning more complex path-based bug
semantics. Our code and data are available at
https://figshare.com/s/4a16a528d6874aad51a0.
