  The programming skill is one crucial ability for Large Language Models
(LLMs), necessitating a deep understanding of programming languages (PLs) and
their correlation with natural languages (NLs). We examine the impact of
pre-training data on code-focused LLMs' performance by assessing the comment
density as a measure of PL-NL alignment. Given the scarcity of code-comment
aligned data in pre-training corpora, we introduce a novel data augmentation
method that generates comments for existing code, coupled with a data filtering
strategy that filters out code data poorly correlated with natural language. We
conducted experiments on three code-focused LLMs and observed consistent
improvements in performance on two widely-used programming skill benchmarks.
Notably, the model trained on the augmented data outperformed both the model
used for generating comments and the model further trained on the data without
augmentation.
