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。

ARIST: 一种有效的 API 参数推荐方法

ARIST: An Effective API Argument Recommendation Approach

Academic research in recommender systems has been greatly focusing on the
accuracy-related measures of recommendations. Even when non-accuracy measures
such as popularity bias, diversity, and novelty are studied, it is often solely
from the users' perspective. However, many real-world recommenders are often
multi-stakeholder environments in which the needs and interests of several
stakeholders should be addressed in the recommendation process. In this paper,
we focus on the popularity bias problem which is a well-known property of many
recommendation algorithms where few popular items are over-recommended while
the majority of other items do not get proportional attention and address its
impact on different stakeholders. Using several recommendation algorithms and
two publicly available datasets in music and movie domains, we empirically show
the inherent popularity bias of the algorithms and how this bias impacts
different stakeholders such as users and suppliers of the items. We also
propose metrics to measure the exposure bias of recommendation algorithms from
the perspective of different stakeholders.

本文通过使用多个推荐算法和音乐、电影两个领域的公开数据集，实证了推荐算法的内在流行度偏差以及这种偏差对用户和项目供应商等不同利益相关者的影响，并提出了从不同利益相关者的角度衡量推荐算法暴露偏差的度量方法。