Action Word Prediction for Neural Source Code Summarization

TL;DR

This paper emphasizes the importance of accurately predicting action words in source code summaries to improve the quality of automatic code documentation, highlighting current challenges and future directions.

Contribution

It introduces action word prediction as a focused problem in code summarization, analyzing baseline performance and offering recommendations for enhancing this aspect.

Findings

Action word prediction is crucial for meaningful code summaries.

Current baselines show room for improvement in action word accuracy.

Recommendations for future research are provided.

Abstract

Source code summarization is the task of creating short, natural language descriptions of source code. Code summarization is the backbone of much software documentation such as JavaDocs, in which very brief comments such as "adds the customer object" help programmers quickly understand a snippet of code. In recent years, automatic code summarization has become a high value target of research, with approaches based on neural networks making rapid progress. However, as we will show in this paper, the production of good summaries relies on the production of the action word in those summaries: the meaning of the example above would be completely changed if "removes" were substituted for "adds." In this paper, we advocate for a special emphasis on action word prediction as an important stepping stone problem towards better code summarization -- current techniques try to predict the action word along with the whole summary, and yet action word prediction on its own is quite difficult. We show the value of the problem for code summaries, explore the performance of current baselines, and provide recommendations for future research.