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Software comments sometimes are not promptly updated in sync when the associated code is changed. The inconsistency between code and comments may mislead the developers and result in future bugs. Thus, studies concerning code-comment synchronization have become highly important, which aims to automatically synchronize comments with code changes. Existing code-comment synchronization approaches mainly contain two types, i.e., (1) deep learning-based (e.g., CUP), and (2) heuristic-based (e.g., HebCUP). The former constructs a neural machine translation-structured semantic model, which has a more generalized capability on synchronizing comments with software evolution and growth. However, the latter designs a series of rules for performing token-level replacements on old comments, which can generate the completely correct comments for the samples fully covered by their fine-designed …