Learning and remembering to use APIs are difficult. Several techniques have
been proposed to assist developers in using APIs. Most existing techniques
focus on recommending the right API methods to call, but very few techniques
focus on recommending API arguments. In this paper, we propose ARIST, a novel
automated argument recommendation approach which suggests arguments by
predicting developers' expectations when they define and use API methods. To
implement this idea in the recommendation process, ARIST combines program
analysis (PA), language models (LMs), and several features specialized for the
recommendation task which consider the functionality of formal parameters and
the positional information of code elements (e.g., variables or method calls)
in the given context. In ARIST, the LMs and the recommending features are used
to suggest the promising candidates identified by PA. Meanwhile, PA navigates
the LMs and the features working on the set of the valid candidates which
satisfy syntax, accessibility, and type-compatibility constraints defined by
the programming language in use. Our evaluation on a large dataset of
real-world projects shows that ARIST improves the state-of-the-art approach by
19% and 18% in top-1 precision and recall for recommending arguments of
frequently-used libraries. For general argument recommendation task, i.e.,
recommending arguments for every method call, ARIST outperforms the baseline
approaches by up to 125% top-1 accuracy. Moreover, for newly-encountered
projects, ARIST achieves more than 60% top-3 accuracy when evaluating on a
larger dataset. For working/maintaining projects, with a personalized LM to
capture developers' coding practice, ARIST can productively rank the expected
arguments at the top-1 position in 7/10 requests.

本文提出了一种基于 API 方法使用者行为预测的自动参数推荐方法 ——ARIST，通过结合程序分析、语言模型以及具有专门化的功能的参数推荐特性，获取作为候选者的有前途参数，并对其进行评估，实验结果表明 ARIST 在 API 参数推荐方面相比现有方法有着更优秀的表现。