# Automatically Extracting Subroutine Summary Descriptions from Unstructured Comments

arXiv: https://arxiv.org/abs/1912.10198 · HTML: https://mlsanity.com/paper/1912.10198/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [Meta Learning for Code Summarization](https://mlsanity.com/paper/2201.08310/) (2022)

## Abstract (arXiv)

Summary descriptions of subroutines are short (usually one-sentence) natural language explanations of a subroutine's behavior and purpose in a program. These summaries are ubiquitous in documentation, and many tools such as JavaDocs and Doxygen generate documentation built around them. And yet, extracting summaries from unstructured source code repositories remains a difficult research problem -- it is very difficult to generate clean structured documentation unless the summaries are annotated by programmers. This becomes a problem in large repositories of legacy code, since it is cost prohibitive to retroactively annotate summaries in dozens or hundreds of old programs. Likewise, it is a problem for creators of automatic documentation generation algorithms, since these algorithms usually must learn from large annotated datasets, which do not exist for many programming languages. In this paper, we present a semi-automated approach via crowdsourcing and a fully-automated approach for annotating summaries from unstructured code comments. We present experiments validating the approaches, and provide recommendations and cost estimates for automatically annotating large repositories.