Dependency-Aware Method Naming Framework with Generative Adversarial Sampling

Chenjie Shen, Jie Zhu, Lei Yu, Li Yang, Chun Zuo

Abstract: Method naming plays a vital role in code readability and maintenance. Researchers have proposed various approaches to automate method name recommendation and consistency checking task. However, two issues still remain unsolved: 1) Current work mainly focuses on local implementation and class-enclosed contexts, while dependency information is not fully exploited for the method name recommendation (MNR) task. 2) As a binary classification task, the method name consistency checking (MCC) task lacks high-quality negative samples severely, posing a challenge to train model. In this paper, we propose DMNA, a method naming framework with dependencies and generative adversarial sampling, which could help alleviate the above-mentioned problems. First, we introduce dependency information with other method contexts into training, which helps improve the performance of MNR task. Second, we leverage the model tuned for MNR task to generate high-quality adversarial samples for MCC task. Finally, we utilize prompt tuning to align the downstream task objective with the pre-training task, which helps alleviate the discrepancy problem and exploit the potential of pre-trained models. We validate the effectiveness of our approach on five widely-adopted datasets. Experimental results show that DMNA scores 49.1%, 58.5%, 63.5%, 75.4% on exact match accuracy for four MNR datasets, outperforming the SoTA baseline by at least 6.2%. And DMNA improves the accuracy of MCC task from 80.8% to 81.8%.