Software developers often write code that has similar functionality to existing code segments.A code recommendation tool that helps developers reuse these code fragments can significantly improve their efficiency.Several methods have been proposed in recent years.Some use sequence matching algorithms to find the related recommendations.Most of these methods are time-consuming and can leverage only low-level textual information from code.Others extract features from code and obtain similarity using numerical feature vectors.However,the similarity of feature vectors is often not equivalent to the original code's similarity.Structural information is lost during the process of transforming abstract syntax trees into vectors.We propose an approximate sub-tree matching based method to solve this problem.Unlike existing tree-based approaches that match feature vectors,it retains the tree structure of the query code in the matching process to find code fragments that best match the current query.It uses a fast approximation sub-tree matching algorithm by transforming the sub-tree matching problem into the match between the tree and the list.In this way,the structural information can be used for code recommendation tasks that have high time requirements.We have constructed several real-world code databases covering different languages and granularities to evaluate the effectiveness of our method.The results show that our method outperforms two compared methods,SENSORY and Aroma,in terms of the recall value on all the datasets,and can be applied to large datasets.

Yichao SHAO;Zhiqiu HUANG;Weiwei LI;Yaoshen YU

School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211100,China;Key Laboratory of Safety-Critical Software,Ministry of Industry and Information Technology,Nanjing 211100,China;Collaborative Innovation Center of Novel Software Technology and Industrialization,Nanjing 210016,China

信息与电子工程前沿（英文）

[1]Yichao SHAO;Zhiqiu HUANG;Weiwei LI;Yaoshen YU-.Fast code recommendation via approximate sub-tree matching)[J].信息与电子工程前沿（英文）,2022(08):1205-1216

granularities,SENSORY,Aroma

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