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

TL;DR

This paper investigates popularity bias in recommender systems for third-party libraries, revealing limited research on the topic and proposing a mechanism to mitigate this bias, supported by empirical analysis.

Contribution

It provides a comprehensive assessment of existing TPL RSSEs regarding popularity bias and introduces a new mechanism to reduce this bias in recommendations.

Findings

Popularity bias is underexplored in TPL RSSEs.

Most existing systems do not effectively address popularity bias.

A proposed mechanism can help mitigate popularity bias.

Abstract

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.