Project-Level Encoding for Neural Source Code Summarization of Subroutines

• URL: http://arxiv.org/abs/2103.11599v1
• Date: Mon, 22 Mar 2021 06:01:07 GMT
• Title: Project-Level Encoding for Neural Source Code Summarization of Subroutines
• Authors: Aakash Bansal, Sakib Haque, Collin McMillan
• Abstract summary: We present a project-level encoder to improve models of code summarization. We use that representation to augment the encoder of state-of-the-art neural code summarization techniques.
• Score: 6.939768185086755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Source code summarization of a subroutine is the task of writing a short, natural language description of that subroutine. The description usually serves in documentation aimed at programmers, where even brief phrase (e.g. "compresses data to a zip file") can help readers rapidly comprehend what a subroutine does without resorting to reading the code itself. Techniques based on neural networks (and encoder-decoder model designs in particular) have established themselves as the state-of-the-art. Yet a problem widely recognized with these models is that they assume the information needed to create a summary is present within the code being summarized itself - an assumption which is at odds with program comprehension literature. Thus a current research frontier lies in the question of encoding source code context into neural models of summarization. In this paper, we present a project-level encoder to improve models of code summarization. By project-level, we mean that we create a vectorized representation of selected code files in a software project, and use that representation to augment the encoder of state-of-the-art neural code summarization techniques. We demonstrate how our encoder improves several existing models, and provide guidelines for maximizing improvement while controlling time and resource costs in model size.

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