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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2100379


Fast code recommendation via approximate sub-tree matching


Author(s):  Yichao SHAO, Zhiqiu HUANG, Weiwei LI, Yaoshen YU

Affiliation(s):  School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China; more

Corresponding email(s):   zqhuang@nuaa.edu.cn

Key Words:  Code reuse, Code recommendation, Tree similarity, Structure information


Yichao SHAO, Zhiqiu HUANG, Weiwei LI, Yaoshen YU. Fast code recommendation via approximate sub-tree matching[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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author="Yichao SHAO, Zhiqiu HUANG, Weiwei LI, Yaoshen YU",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100379"
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Abstract: 
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 subtree matching algorithm by transforming the subtree 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 to evaluate the effectiveness of our method, which covers different languages and granularities. The results show that our method outperforms two compared methods in terms of recall value on all the datasets, and can be applied to big datasets.

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