Automatic code comment generation aims to produce concise natural language summaries that describe the functionality and intent of source code. Recent transformer-based models such as CodeBERT have demonstrated strong performance on this task through large-scale pretraining on code–text pairs. However, traditional supervised fine-tuning often fails to capture deeper semantic relationships within source code, leading to comments that are grammatically correct but semantically shallow. The study investigates whether contrastive fine-tuning of the CodeBERT encoder can enhance its downstream capability for comment generation. Two contrastive strategies are explored: a code–code alignment, based on semantically equivalent code snippets, and a code–diff alignment, based on fine-grained code edits from version control systems.