# Recommendations for Datasets for Source Code Summarization

## TL;DR

This paper discusses the importance of standardized datasets for source code summarization, introduces a large dataset of Java methods with descriptions, and provides recommendations to improve research reproducibility.

## Contribution

It offers guidelines for dataset creation in code summarization and releases a comprehensive dataset of Java methods with descriptions to support future research.

## Key findings

- Performance varies over 33% due to dataset design differences
- The released dataset contains over 2.1 million Java method-description pairs
- Key differences between code and natural language data are highlighted.

## Abstract

Source Code Summarization is the task of writing short, natural language descriptions of source code. The main use for these descriptions is in software documentation e.g. the one-sentence Java method descriptions in JavaDocs. Code summarization is rapidly becoming a popular research problem, but progress is restrained due to a lack of suitable datasets. In addition, a lack of community standards for creating datasets leads to confusing and unreproducible research results -- we observe swings in performance of more than 33% due only to changes in dataset design. In this paper, we make recommendations for these standards from experimental results. We release a dataset based on prior work of over 2.1m pairs of Java methods and one sentence method descriptions from over 28k Java projects. We describe the dataset and point out key differences from natural language data, to guide and support future researchers.