Pre-Implementation Method Name Prediction for Object-Oriented Programming

Shangwen,Wang1; Ming,Wen2; Bo,Lin1; Yepang,Liu3; Tegawendé F.,Bissyandé4; Xiaoguang,Mao1

2023-05-13

10.1145/3597203

ACM Transactions on Software Engineering and Methodology   影响因子和分区

1049-331X

1557-7392

Method naming is a challenging development task in object-oriented programming. In recent years, several research efforts have been undertaken to provide automated tool support for assisting developers in this task. In general, literature approaches assume the availability of method implementation to infer its name. Methods, however, are usually named before their implementations. In this work, we fill the gap in the literature about method name prediction by developing an approach that predicts the names of all methods to be implemented within a class. Our work considers the class name as the input: The overall intuition is that classes with semantically similar names tend to provide similar functionalities, and hence similar method names. We first conduct a large-scale empirical analysis on 258K+ classes from real-world projects to validate our hypotheses. Then, we propose a hybrid big code-driven approach, Mario, to predict method names based on the class name: We combine a deep learning model with heuristics summarized from code analysis. Extensive experiments on 22K+ classes yielded promising results: compared to the state-of-the-art code2seq model (which leverages method implementation data), our approach achieves comparable results in terms of F-score at token-level prediction; our approach, additionally, outperforms code2seq in prediction at the name level. We further show that our approach significantly outperforms several other baselines.

Method name prediction naming convention

SCI ; EI

英语

其他

National Natural Science Foundation of China["62002125","61932021"] ; Young Elite Scientists Sponsorship Program by CAST[2021QNRC001] ; European Research Council (ERC) under the European Union[949014]

Computer Science

Computer Science, Software Engineering

WOS:001085699500024

Association for Computing Machinery (ACM)

20234314960355

Codes (symbols) ; Deep learning ; Heuristic methods ; Object oriented programming

Ergonomics and Human Factors Engineering:461.4 ; Computer Programming:723.1 ; Data Processing and Image Processing:723.2

COMPUTER SCIENCE

人工提交

在线出版

1.National University of Defense Technology
2.Huazhong University of Science and Technology
3.Southern University of Science and Technology
4.University of Luxembourg

Shangwen,Wang,Ming,Wen,Bo,Lin,et al. Pre-Implementation Method Name Prediction for Object-Oriented Programming[J]. ACM Transactions on Software Engineering and Methodology,2023,32(6).

Shangwen,Wang,Ming,Wen,Bo,Lin,Yepang,Liu,Tegawendé F.,Bissyandé,&Xiaoguang,Mao.(2023).Pre-Implementation Method Name Prediction for Object-Oriented Programming.ACM Transactions on Software Engineering and Methodology,32(6).

Shangwen,Wang,et al."Pre-Implementation Method Name Prediction for Object-Oriented Programming".ACM Transactions on Software Engineering and Methodology 32.6(2023).