计算机科学技术学报 ›› 2019,Vol. 34 ›› Issue (5): 993-1006.doi: 10.1007/s11390-019-1956-2

所属专题： Data Management and Data Mining； Software Systems

• Special Section on Software Systems 2019 • 上一篇    下一篇

基于图嵌入的API子图推荐

Chun-Yang Ling, Yan-Zhen Zou*, Member, CCF, ACM, IEEE, Ze-Qi Lin, Bing Xie, Senior Member, CCF   

  1. 1 Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing 100871, China;
    2 School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
  • 收稿日期:2019-02-26 修回日期:2019-07-12 出版日期:2019-08-31 发布日期:2019-08-31
  • 通讯作者: Yan-Zhen Zou E-mail:zouyz@pku.edu.cn
  • 作者简介:Chun-Yang Ling received his B.S. degree in computer science from Peking University, Beijing, in 2018. He is now a Master student in computer science at Peking University, Beijing. His current research interests include software engineering, knowledge engineering, and data mining.
  • 基金资助:
    The work was supported by the National Natural Science Fund for Distinguished Young Scholars of China under Grant No. 61525201.

Graph Embedding Based API Graph Search and Recommendation

Chun-Yang Ling, Yan-Zhen Zou*, Member, CCF, ACM, IEEE, Ze-Qi Lin, Bing Xie, Senior Member, CCF   

  1. 1 Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing 100871, China;
    2 School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
  • Received:2019-02-26 Revised:2019-07-12 Online:2019-08-31 Published:2019-08-31
  • Contact: Yan-Zhen Zou E-mail:zouyz@pku.edu.cn
  • About author:Chun-Yang Ling received his B.S. degree in computer science from Peking University, Beijing, in 2018. He is now a Master student in computer science at Peking University, Beijing. His current research interests include software engineering, knowledge engineering, and data mining.
  • Supported by:
    The work was supported by the National Natural Science Fund for Distinguished Young Scholars of China under Grant No. 61525201.

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检索软件项目API（Application Program Interface，应用程序接口）是开发者复用软件的重要方式.当前基于自然语言的API检索和推荐主要面临以下两个挑战：1）随着软件项目变得越来越复杂，自然语言查询的准确性需要提高.2）需要展示目标APIs之间的语义关联，以更好地辅助开发者理解APIs的逻辑和使用场景.为此，本文提出了一种基于图嵌入的API子图推荐方法.首先，该方法能够基于软件项目源代码，自动构建其代码结构图，并通过图嵌入对源代码进行表示.然后，开发者可以输入自然语言问题、检索并返回相关的APIs及其关联信息构成的连通API子图，从而提高了API检索和复用的效率.本文选择了开源项目Apache Lucene、POI、JodaTime为例及进行实验验证.结果表明本文方法的F1值相比现有的基于最短路径的方法提高了10%，而平均响应时间缩短了约60倍.

关键词: API推荐, 代码图, 图嵌入, 软件复用

Abstract: Searching application programming interfaces (APIs) is very important for developers to reuse software projects. Existing natural language based API search mainly faces the following challenges. 1) More accurate results are required as software projects evolve to be more heterogeneous and complex. 2) The semantic relationships between APIs (e.g., inheritances between classes, and invocations between methods) need to be illustrated so that developers can better understand their usage scenarios. To deal with these issues, we propose GeAPI, a novel graph embedding based approach for API graph search and recommendation in this paper. First, we build a software project's API graph automatically from its source code and represent each API using graph embedding methods. Second, we search the API graph with a question in natural language, and return the corresponding subgraph that is composed of relevant code elements and their associated relationships, as the best answer of the question. In experiments, we select three well-known open source projects, JodaTime, Apache Lucene and POI, as examples to perform API search tasks. The experimental results show that our approach GeAPI improves F1-score by 10% compared with the existing shortest path based API search approach, while reduces the average response time about 60 times.

Key words: application programming interface (API) recommendation, API graph, graph embedding, software reuse

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