Embedding API dependency graph for neural code generation

The problem of code generation from textual program descriptions has long been viewed as a grand challenge in software engineering. In recent years, many deep learning based approaches have been proposed, which can generate a sequence of code from a sequence of textual program description. However, the existing approaches ignore the global relationships among API methods, which are important for understanding the usage of APIs. In this paper, we propose to model the dependencies among API methods as an API dependency graph (ADG) and incorporate the graph embedding into a sequence-to-sequence (Seq2Seq) model. In addition to the existing encoder-decoder structure, a new module named “embedder” is introduced. In this way, the decoder can utilize both global structural dependencies and textual program description to predict the target code. We conduct extensive code generation experiments on three public datasets and in two programming languages (Python and Java). Our proposed approach, called ADGChen Lyu ( ) E-mail: lvchen@sdnu.edu.cn Ruyun Wang E-mail: ruyunw@outlook.com Hongyu Zhang E-mail: hongyu.zhang@newcastle.edu.au Hanwen Zhang E-mail: zhanghanwen0726@gmail.com Songlin Hu E-mail: husonglin@iie.ac.cn 1.School of Information Science and Engineering, Shandong Normal University, Jinan, China. 2.The University of Newcastle, Callaghan, NSW, Australia. 3.Big Data Center of Shandong Province, Jinan, China. 4.Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China. 5.School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China. ar X iv :2 10 3. 15 36 1v 1 [ cs .S E ] 2 9 M ar 2 02 1