  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.
