基于情境感知的API个性化推荐

Context-aware Based API Personalized Recommendation

在软件开发的过程中,开发人员在遇到编程困境时通常会检索合适的API来完成编程任务。情境信息和开发者画像在有效的API推荐中起着至关重要的作用,却在很大程度上被忽视了。因而文中提出了一种基于情境感知的API个性化推荐方法。该方法利用程序静态分析技术,对代码文件做抽象语法树解析,提取信息构建代码库,并对开发者API使用偏好建模。然后计算开发者当前查询语句与历史代码库中查询的语义相似度,检索出top-k个相似历史查询。最终利用查询语句信息、方法名信息、情境信息以及开发者API使用偏好信息对API进行重排序并推荐给开发者。通过模拟编程任务开发的不同阶段,使用MRR,MAP,Hit, NDCG评估指标来验证所提方法的有效性。实验结果表明,所提方法的API推荐效果优于基准方法,能够为开发者推荐更想要的API。

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.