Context-based Python Code Recommendation

Code recommendation plays a crucial role in programming, assisting programmers in improving code quality, efficiency, and maintainability, thereby better addressing complex software development tasks. In recent years, artificial intelligence has been widely applied in software engineering domains such as code recommendation, particularly in the field of static languages, where significant advancements have been made in intelligent code recommendation. However, for dynamic languages like Python, intelligent code recommendation faces numerous challenges, such as the difficulties in dynamic analysis and code data imbalance. In light of this, building upon previous work, this paper achieves more accurate Python code recommendation by representing code context with semantically richer graphs and employing conditional generative adversarial networks for data augmentation of code semantic graphs, mitigating the impact of data imbalance. Experimental results demonstrate that the proposed code recommendation method, GraphPyRec, can effectively recommend Python code, and the data augmentation method, GraphGAN, can significantly optimize the recommendation model.