A Two-phase Recommendation Framework for Consistent Java Method Names
This addresses the problem of improving code readability and maintainability for software developers, though it appears incremental as it builds on existing neural network techniques.
The paper tackles the problem of recommending consistent Java method names in software engineering by proposing a two-phase framework that first classifies method categories using a fast text neural network, then recommends names using LSTM networks. The approach significantly outperforms state-of-the-art methods on a dataset of nearly 8 million Java methods.
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.