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Recommender systems for software engineering (RSSEs) assist softwareengineers in dealing with a growing information overload when discerningalternative development solutions. While RSSEs are becoming more and moreeffective in suggesting handy recommendations, they tend to suffer frompopularity bias, i.e., favoring items that are relevant mainly because severaldevelopers are using them. While this rewards artifacts that are likely morereliable and well-documented, it would also mean that missing artifacts arerarely used because they are very specific or more recent. This paper studiespopularity bias in Third-Party Library (TPL) RSSEs. First, we investigatewhether state-of-the-art research in RSSEs has already tackled the issue ofpopularity 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 recommendationlist. The empirical study reveals that the issue of dealing with popularity inTPL RSSEs has not received adequate attention from the software engineeringcommunity. Among the surveyed work, only one starts investigating the issue,albeit getting a low prediction performance.

Dealing with Popularity Bias in Recommender Systems for Third-party Libraries: How far Are We?