Abstract: Application Programming Interfaces (API) play an important role in modern software development,and developers often need to search for the appropriate API for their programming tasks.However,with the development of the information industry,API reference documents have become larger and larger,and traditional search methods have also caused inconvenience to engineers' queries because of redundant and erroneous information on the Internet.At the same time,due to the vocabulary and knowledge gap between the natural language description of programming tasks and the description in the API documentation,it is difficult to find a suitable API.Based on these issues,this paper proposes an algorithm called ARDSQ (Recommendation base on Documentation and Solved Question) which is an API recommendation algorithm that integrates domain knowledge.ARDSQ can retrieve the closest API in the knowledge base based on the natural language description given by the engineer.Experiments show that,compared with two advanced API recommendation algorithms(BIKER,DeepAPILearning),ARDSQ has greater advantages in the key evaluation index (Hit-n,MRR,MAP ) of the recommendation system.

Key words: API, Code recommendation, Deep learning, Information retrieval, Program analysis