Just-In-Time Method Name Updating With Heuristics and Neural Model

Ensuring the quality and conciseness of method names is pivotal for the readability and maintainability of source code. However, for developers, it often presents challenges, particularly during the course of code evolution. Throughout this process, developers sometimes may neglect to update the method name, resulting in inconsistency which could potentially mislead developers and introduce future bugs. In this paper, we propose the task of “Just-In-Time (JIT) Method Name Updating” which automatically performs method name updates to avoid inconsistent names and fix them before being introduced into code bases. Specifically, we propose an approach that combines heuristic rules and a neural model. The heuristic rule-based component mainly focuses on the single-token changes for our empirical findings that the proportion of single-token modifications is extensive, and often corresponds to code-indicative updates. The neural model-based component is a customized Seq2seq model considering code changes and the new method body’s token type. To evaluate our approach, we conduct extensive experiments on the collected dataset with over 108K method name-body co-change samples from popular Java projects. The results show that our method outperforms the three baselines on all metrics. In particular, our approach achieves a significant improvement in Accuracy and improves method generation baseline by 23.5%.