A Two-phase Recommendation Framework for Consistent Java Method Names

TL;DR

This paper presents a two-phase framework for recommending consistent Java method names, combining a fast classifier for method categories with LSTM networks for name generation, significantly improving over existing methods.

Contribution

The novel two-phase approach integrates fast classification and sequence modeling to enhance method name recommendation accuracy.

Findings

Outperforms state-of-the-art methods in accuracy

Uses nearly 8 million Java methods for evaluation

Employs LSTM networks for name consistency

Abstract

In software engineering (SE) tasks, the naming approach is so important that it attracts many scholars from all over the world to study how to improve the quality of method names. To accurately recommend method names, we employ a novel framework to handle this problem. In our expeirments, nearly 8 million Java methods are collected from open source organizations as our evaluation dataset. In the first-phase recommendation, we introduce a fast and simple classifier based on the fast text neural network for reccomending potential method category. In the second-phase recomendation, we employ both two Long Short Term Memory Networks to reccomend consitent method names from each classification. Evaluation results prove that the proposed approach significantly outperforms state-of-the-art approach.