A Transformer-based Approach for Source Code Summarization

Understand

Generating a readable summary that describes the functionality of a program is known as source code summarization.

• In this task, learning code representation by modeling the pairwise relationship between code tokens to capture their long-range dependencies is crucial.
• To learn code representation for summarization, we explore the Transformer model that uses a self-attention mechanism and has shown to be effective in capturing long-range dependencies.
• In this work, we show that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin.