Recommendations for Datasets for Source Code Summarization
This work tackles dataset standardization for researchers in software documentation and AI, but it is incremental as it builds on prior work without introducing new methods.
The paper addresses the lack of standardized datasets in source code summarization, which causes performance swings of over 33% due to dataset design issues, and releases a dataset of over 2.1 million Java method-description pairs from 28k projects to guide future research.
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.