Recommender systems for software engineering (RSSEs) assist software engineers in dealing with a growing information overload when discerning alternative development solutions. While RSSEs are becoming more and more effective in suggesting handy recommendations, they tend to suffer from popularity bias, i.e., favoring items that are relevant mainly because several developers are using them. While this rewards artifacts that are likely more reliable and well-documented, it would also mean that missing artifacts are rarely used because they are very specific or more recent. This paper studies popularity bias in Third-Party Library (TPL) RSSEs. First, we investigate whether state-of-the-art research in RSSEs has already tackled the issue of popularity bias. Then, we quantitatively assess four existing TPL RSSEs, exploring their capability to deal with the recommendation of popular items. Finally, we propose a mechanism to defuse popularity bias in the recommendation list. The empirical study reveals that the issue of dealing with popularity in TPL RSSEs has not received adequate attention from the software engineering community. Among the surveyed work, only one starts investigating the issue, albeit getting a low prediction performance.

翻译：软件工程推荐系统（RSSEs）帮助软件工程师在识别替代开发方案时应对日益增长的信息过载问题。尽管RSSEs在提供便捷推荐方面愈发有效，但它们往往面临流行度偏差问题，即倾向于推荐主要因大量开发者使用而具有相关性的项目。虽然这会使更可靠且文档完善的工件获得青睐，但也意味着缺失的工件（如高度专业化或新近出现的工件）因使用稀少而被忽视。本文研究了第三方库（TPL）推荐系统中的流行度偏差。首先，我们探究现有RSSEs研究是否已解决流行度偏差问题；其次，定量评估四种现有TPL推荐系统处理热门项目推荐的能力；最后，提出一种缓解推荐列表中流行度偏差的机制。实证研究表明，软件工程社区尚未充分关注TPL推荐系统中流行度处理这一议题。在已调研的工作中，仅有一项研究开始探讨此问题，但其预测性能较低。