Graph Embedding Based API Graph Search and Recommendation

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...

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Veröffentlicht in:Journal of computer science and technology Jg. 34; H. 5; S. 993 - 1006
Hauptverfasser: Ling, Chun-Yang, Zou, Yan-Zhen, Lin, Ze-Qi, Xie, Bing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.09.2019
Springer
Springer Nature B.V
Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing 100871, China School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
Schlagworte:
Application programming interface
Applications programming
Artificial Intelligence
Computer Science
Data Structures and Information Theory
Embedding
Graph theory
Information Systems Applications (incl.Internet)
Natural language
Questions
Regular Paper
Searching
Software
Software Engineering
Software reuse
Source code
Theory of Computation
application programming interface (API) recommendation
API graph
graph embedding
software reuse
ISSN:1000-9000, 1860-4749
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-019-1956-2