Related papers: The Role of Data Filtering in Open Source Software…

As reproducibility becomes a greater concern, conferences have largely converged to a strategy of asking reviewers to indicate whether code was attached to a submission. This is part of a larger trend of taking action based on assumed…

Design-level decisions in open-source software (OSS) projects are often made through structured mechanisms such as proposals, which require substantial community discussion and review. Despite their importance, the proposal process is…

Popularity bias is a well-known issue in recommender systems where few popular items are over-represented in the input data, while majority of other less popular items are under-represented. This disparate representation often leads to bias…

Mobile app stores produce a tremendous amount of data in the form of user reviews, which is a huge source of user requirements and sentiments; such reviews allow app developers to proactively address issues in their apps. However, only a…

Items popularity is a strong signal in recommendation algorithms. It strongly affects collaborative filtering approaches and it has been proven to be a very good baseline in terms of results accuracy. Even though we miss an actual…

Traceability between published scientific breakthroughs and their implementation is essential, especially in the case of open-source scientific software which implements bleeding-edge science in its code. However, aligning the link between…

Refactoring is the process of changing the internal structure of software to improve its quality without modifying its external behavior. Empirical studies have repeatedly shown that refactoring has a positive impact on the…

Collaborative filtering is a popular technique to infer users' preferences on new content based on the collective information of all users preferences. Recommender systems then use this information to make personalized suggestions to users.…

Popularity bias is the idea that a recommender system will unduly favor popular artists when recommending artists to users. As such, they may contribute to a winner-take-all marketplace in which a small number of artists receive nearly all…

Recommendation and ranking systems are known to suffer from popularity bias; the tendency of the algorithm to favor a few popular items while under-representing the majority of other items. Prior research has examined various approaches for…

Data from software repositories have become an important foundation for the empirical study of software engineering processes. A recurring theme in the repository mining literature is the inference of developer networks capturing e.g.…

Software engineering and information systems practices seek ultimately to create the flawless product. One of the tools used to improve the quality of software development is the use of metrics. In this paper, metrics retrieved from open…

Almost every Mining Software Repositories (MSR) study requires, as first step, the selection of the subject software repositories. These repositories are usually collected from hosting services like GitHub using specific selection criteria…

Gender diversity in open source software development continues to be a topic of growing interest among researchers, practitioners, and organizations. To date, research has revealed disparities in participation between developers on the…

Mathematical researchers, especially those in early-career positions, face critical decisions about topic specialization with limited information about the collaborative environments of different research areas. The aim of this paper is to…

Developers usually select different open source licenses to restrain the conditions of using open source software, in order to protect intellectual property rights effectively and maintain the long-term development of the software. However,…

Quantum computing is rapidly advancing, but quantum software development faces significant challenges, including a steep learning curve, high hardware error rates, and a lack of mature engineering practices. This study conducts a…

As society becomes increasingly reliant on artificial intelligence, the need to mitigate risk and harm is paramount. In response, researchers and practitioners have developed tools to detect and reduce undesired bias, commonly referred to…

Selecting third-party software packages in open-source ecosystems like Python is challenging due to the large number of alternatives and limited transparent evidence for comparison. Generative AI tools are increasingly used in development…

Due to the voluntary nature of open source software, it can be hard to find a developer to work on a particular task. For example, some issue reports may be too cumbersome and unexciting for someone to volunteer to do them, yet these issue…