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This paper presents a multi-granularity method for the task of source code summarization, which generates a concise functional description for the given code snippet. We notice that skilled programmers write and read source codes hierarchically and pay close attention to conceptual entities like statements, tokens, sub-tokens, and the mapping relations between them. The entities have specific emphasis according to their granularities, e.g., statements in coarse-granularity re- veal the global logical semantics of code, and the sub-tokens in fine-granularity are more related to the textual semantics. Driven by this observation, we argue that a multi-granularity formulation incorporating these conceptual entities may benefit the code summarization task. Concretely, the source code is trans- formed into a pyramidal representation, and then a pyramid attention mechanism is applied for efficient feature aggregation among different hierarchies in it. We instantiate our multi-granularity method using the proposed pyramid attention and name it PA-former (Pyramid Attention transformer), which is evaluated on two source code summarization benchmarks where it surpasses the prior works and achieves new state-of-the-art results. Our code and data are available at https://github.com/leichainju/pa-former.
Author Information
Lei Chai (Nanjing University)
Ming LI (Nanjing University)
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