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Automatically generating human-readable text describing the functionality ofa program is the intent of source code summarization. Although Neural LanguageModels achieve significant performance in this field, an emerging trend iscombining neural models with external knowledge. Most previous approaches relyon the sentence-level retrieval and combination paradigm (retrieval of similarcode snippets and use of the corresponding code and summary pairs) on theencoder side. However, this paradigm is coarse-grained and cannot directly takeadvantage of the high-quality retrieved summary tokens on the decoder side. Inthis paper, we explore a fine-grained token-level retrieval-augmented mechanismon the decoder side to help the vanilla neural model generate a better codesummary. Furthermore, to mitigate the limitation of token-level retrieval oncapturing contextual code semantics, we propose to integrate code semanticsinto summary tokens. Extensive experiments and human evaluation reveal that ourtoken-level retrieval-augmented approach significantly improves performance andis more interpretive.

Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization