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Related Experiment Video

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Author Spotlight: Exploring Glial Influence in Experience-Dependent Synaptic Pruning During Critical Periods

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Looking for related posts on GitHub discussions.

Marcia Lima^1,2, Igor Steinmacher³, Denae Ford⁴

¹Department of Computer Science, Amazonas State University (UEA), Manaus, Amazonas, Brazil.

Peerj. Computer Science

|December 11, 2023

Summary

This study introduces RD-Detector, a tool using Sentence-BERT to find duplicate or similar posts on GitHub Discussions. This helps reduce noise and improve knowledge sharing in software development communities.

Keywords:

Communication tool GitHub Discussions Knowledge sharing Related posts Sentence-BERT Software teams interaction

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Area of Science:

Software Engineering
Human-Computer Interaction
Natural Language Processing

Background:

Software development relies on collaborative tools, with forums like GitHub Discussions gaining prominence.
Related posts (duplicates, near-duplicates) on these platforms create noise and hinder knowledge sharing.

Purpose of the Study:

To address the challenge of detecting related posts within GitHub Discussions.
To propose and evaluate an automated approach for identifying duplicated and near-duplicated content.

Main Methods:

Developed RD-Detector, an approach leveraging a Sentence-BERT pre-trained model.
Evaluated RD-Detector on 16,048 discussion posts from three GitHub communities.
Manual evaluation by software maintainers and researchers.

Main Results:

RD-Detector achieved 77-100% precision and 66% recall in detecting related posts.
Manual validation confirmed the approach's effectiveness.
Maintainers identified practical uses for merging discussions and consolidating information.

Conclusions:

RD-Detector offers an effective solution for identifying related posts on GitHub Discussions.
The tool can significantly reduce the manual effort required by maintainers.
Improved detection of related posts enhances knowledge sharing and platform usability.