Context-aware Based API Personalized Recommendation - CHEN Chen, ZHOU Yu, WANG Yong-chao, HUANG Zhi-qiu (Jisuanji kexue, 2021)
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.