A network-centric approach for estimating trust between open source software developers.

Clicks: 319
ID: 73771
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #822 of 2,056 articles by views in PloS one

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 2,056 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Trust between developers influences the success of open source software (OSS) projects. Although existing research recognizes the importance of trust, there is a lack of an effective and scalable computational method to measure trust in an OSS community. Consequently, OSS project members must rely on subjective inferences based on fragile and incomplete information for trust-related decision making. We propose an automated approach to assist a developer in identifying the trustworthiness of another developer. Our two-fold approach, first, computes direct trust between developer pairs who have interacted previously by analyzing their interactions via natural language processing. Second, we infer indirect trust between developers who have not interacted previously by constructing a community-wide developer network and propagating trust in the network. A large-scale evaluation of our approach on a GitHub dataset consisting of 24,315 developers shows that contributions from trusted developers are more likely to be accepted to a project compared to contributions from developers who are distrusted or lacking trust from project members. Further, we develop a pull request classifier that exploits trust metrics to effectively predict the likelihood of a pull request being accepted to a project, demonstrating the practical utility of our approach.
Reference Key
sapkota2019aplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sapkota, Hitesh;Murukannaiah, Pradeep K;Wang, Yi;
Journal PloS one
Year 2019
DOI
10.1371/journal.pone.0226281
URL
Keywords
meta-analysis systematic review vaginal birth after cesarean trial of labor after cesarean

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.