Recommending Relevant Open Source Projects on GitHub using a Collaborative-Filtering Technique

Guendouz, Mohamed; Amine, Abdelmalek; Hamou, Reda Mohamed

doi:10.4018/ijossp.2015010101

Cited by 9 publications

(4 citation statements)

References 18 publications

(18 reference statements)

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“…For instance, repository recommendation on GitHub [10,11,12] has been studied to recommend relevant repositories to users. There are several ways of evaluating relevance of repositories (e.g., network analysis-based relevance, vector space similarities).…”

Section: Discussion -Research and Applicationmentioning

confidence: 99%

Exploring Identical Users on GitHub and Stack Overflow

Komamizu

Hayase

Amagasa

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Analyzing behaviours of developers in different platforms (in particular, GitHub and Stack Overflow in this paper) can reveal interesting facts related to development activities. There are only few datasets for analysing crossplatform user behaviours, especially across GitHub and Stack Overflow. Users on GitHub and Stack Overflow are identifiable by equivalences of email addresses. In order to increase the number of identifiable users on these datasets, this paper retrieves potentially identifiable users between GitHub and Stack Overflow not relying only on email addresses. This paper employs a classification-based link prediction, which design the user identification problem as a link prediction problem on the bipartite graph consisting of users of GitHub and those of Stack Overflow. With the identification method, this paper generates a probabilistic dataset containing pairs of users with probabilities (or confidences). This paper, as well, publishes the identification tool in order to enable further data generation on appearing datasets of GitHub, Stack Overflow and others. The generated dataset and tool are highly helpful to accelerate researches on mining software repositories.

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Section: Discussion -Research and Applicationmentioning

confidence: 99%

Exploring Identical Users on GitHub and Stack Overflow

Komamizu

Hayase

Amagasa

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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“…Prediction and recommendation in social collaborative platforms have been widely studied. These work can be further categorized into two groups: (i) finding experts to perform a certain platform tasks or activities [1,6,10,19,21,31,37,39,41,42] and (ii) recommending content or activities to users in the platforms [7,9,11,32,34,43]. For work in group (i), there were work which proposed methods to find experts to answer questions in Stack Overflow [6,21,31,37,39], while for GitHub, experts are predicted if they will review pull-requests and code for repositories [19,41,42].…”

Section: Related Workmentioning

confidence: 99%

“…For example, there are work which predict and recommend relevant Stack Overflow questions and answers to aid users in software development [7,32,34]. While for GitHub, researchers have proposed methods to predict which software repositories are more relevant to a target user [9,11,43]. Nevertheless, many of these studies only consider the users' behaviours and interests in a single platform when predicting and recommending user platform activities.…”

mentioning

confidence: 99%

Wisdom in Sum of Parts

Lee

2018

Proceedings of the 10th ACM Conference on Web Science

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In this paper, we proposed a novel framework which uses user interests inferred from activities (a.k.a., activity interests) in multiple social collaborative platforms to predict users' platform activities. Included in the framework are two prediction approaches: (i) direct platform activity prediction, which predicts a user's activities in a platform using his or her activity interests from the same platform (e.g., predict if a user answers a given Stack Overflow question using the user's interests inferred from his or her prior answer and favorite activities in Stack Overflow), and (ii) cross-platform activity prediction, which predicts a user's activities in a platform using his or her activity interests from another platform (e.g., predict if a user answers a given Stack Overflow question using the user's interests inferred from his or her fork and watch activities in GitHub). To evaluate our proposed method, we conduct prediction experiments on two widely used social collaborative platforms in the software development community: GitHub and Stack Overflow. Our experiments show that combining both direct and cross platform activity prediction approaches yield the best accuracies for predicting user activities in GitHub (AUC=0.75) and Stack Overflow (AUC=0.89).

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“…Software forges are especially useful for large OSS projects and OSS ecosystems (Cosentino et al, 2017). The platform GitHub represents the newest generation of software forges, combining traditional capabilities (e.g., free hosting and a version control system) with several social features (Guendouz et al, 2015;Squire, 2014). It also supports the Git version-control system, with its features, social interactions (e.g., bug-tracking, issue-tracking, pull requests support, and profiles), and a powerful GitHub API to provide access to metadata around its hosted software projects (Rashid & Prakash, 2022).…”

Section: Introductionmentioning

confidence: 99%

Organizational Influencers in Open-Source Software Projects

Schreiber

2023

International Journal of Open Source Software and Processes

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Traditional software development is shifting toward the open-source development model, particularly in the current environment of competitive challenges to develop software openly. The author employs a case study approach to investigate how organizations and their affiliated developers collaborate in the open-source software (OSS) ecosystem TensorFlow (TF). The analysis of the artificial intelligence OSS library TF combines social network analysis (SNA) and an examination of archival data by mining software repositories. The study looks at the structure and evolution of code-collaboration among developers and with the ecosystem's organizational networks over the TF lifespan. These involved organizations play a particularly critical role in development. The research also looks at productivity, homophily, development, and diversity among developers. The results deepen the understanding of OSS communities' collaborative developer and organization patterns. Furthermore, the study emphasizes the importance and evolution of social networks, diversity, and productivity in ecosystems.

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Recommending Relevant Open Source Projects on GitHub using a Collaborative-Filtering Technique

Cited by 9 publications

References 18 publications

Exploring Identical Users on GitHub and Stack Overflow

Exploring Identical Users on GitHub and Stack Overflow

Wisdom in Sum of Parts

Organizational Influencers in Open-Source Software Projects

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