所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：Proc Int Comput Software Appl Conf
摘要：Software developers often have to implement unfamiliar programming tasks. When faced with these problems, developers often search online for code snippets as references to learn how to solve the unfamiliar tasks. In recent years, some researchers propose several approaches to use programming context to recommend code snippets. Most of these approaches use information retrieval based techniques and treat code snippets as a set of tokens. However, in code, the smallest meaningful unit is code statement, in general, the line of code. Since these studies did not consider this issue, there is still room for improvement in the code snippets recommendation. In this paper, we propose a code Statement sEquence iNformation baSed cOde snippets Recommendation sYstem (SENSORY). Different from existing token based approaches, SENSORY performs code snippets recommendation at code statement granularity. It uses the Burrows Wheeler Transform algorithm to search relevant code snippets, and uses the structure information to re-rank the results. To evaluate the effectiveness of our proposed method, we construct a code database with 1000000 real world code snippets which contain more than 15000000 lines of code. The experimental results show that SENSORY outperforms the two strong baseline work in terms of precision and NDCG. © 2019 IEEE.
ISSN号：0730-3157
是否译文：否
发表时间：2019-07-01
合写作者：Ai, Lei,黄志球,Li, Weiwei,李伟湋,Zhou, Yu,周玉斐,Yu, Yaoshen
通讯作者：王怀磊
发表时间：2019-07-01