Project-Level Encoding for Neural Source Code Summarization of Subroutines

TL;DR

This paper introduces a project-level encoder that enhances neural source code summarization models by incorporating contextual information from related files within a software project, leading to improved summarization performance.

Contribution

The paper proposes a novel project-level encoding technique that augments existing neural models for code summarization, addressing the limitation of models relying solely on individual code snippets.

Findings

Project-level encoding improves summarization accuracy.

Guidelines for balancing model performance and resource costs.

Enhanced models outperform baseline approaches.

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.