Action Word Prediction for Neural Source Code Summarization
This work addresses the problem of improving the accuracy of action words in source code summaries, which is crucial for software developers to quickly understand code snippets.
This paper identifies action word prediction as a critical sub-problem for neural source code summarization. It demonstrates the importance of accurately predicting action words for generating meaningful summaries and evaluates the performance of existing baselines on this specific task.
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.