Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (12): 100-106.doi: 10.11896/jsjkx.201000127

• Computer Software • Previous Articles     Next Articles

Context-aware Based API Personalized Recommendation

CHEN Chen1, ZHOU Yu1,2, WANG Yong-chao1, HUANG Zhi-qiu1,2   

  1. 1 School of Computer Science and Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211100,China
    2 Key Laboratory of Safety-Critical Software,Ministry of Industry and Information Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211100,China
  • Received:2020-10-22 Revised:2021-03-12 Online:2021-12-15 Published:2021-11-26
  • About author:CHEN Chen,born in 1995,postgra-duate.His main research interests include software evolution analysis,artificial intelligence,and mining software repositories.
    ZHOU Yu,born in 1981,Ph.D,professor.His main research interests mainly include software evolution analysis,mining software repositories,software architecture,and reliability analysis.
  • Supported by:
    National Key R & D Program of China(2018YFB1003900),National Natural Science Foundation of China(61972197),Natural Science Foundation of Jiangsu Province,China(BK20201292),Qing-Lan Project of Jiangsu Province and Foundation of Gra-duate Innovation Center in NUAA(kfjj20191606).

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Abstract: In the process of software development,developers often search for appropriate APIs to complete programming tasks when encountering programming difficulties.Contextual information and developer portraits play a critical role in effective API recommendation,but they are largely overlooked.This paper proposes a novel context-aware based API personalized recommendation approach.This approach leverages program static analysis technology (abstract syntax tree) to parse the code file to extract information to construct the code base and model developer API usage preferences.Then it calculates the semantic simila-rity between the developer's current query and the queries in the historical code base,and retrieves top-k similar historical queries.Finally,it leverages the information of query,method name,context and developer API usage preference to re-rank the candidate APIs and recommend to developers.MRR,MAP,Hit and NDCG are used to verify the effectiveness of the method in dif-ferent stages of simulation programming.The experimental results show that the proposed approach outperforms the baseline me-thod and it is more likely to recommend the APIs that developers want.

Key words: API recommendation, Context awareness, Developer portraits, Program static analysis

CLC Number: 

  • TP391
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