New MIRAGE method enhances MSR dataset analysis with metadata and FAIRness

Researchers have developed MIRAGE, a new method for analyzing Mining Software Repositories (MSR) datasets by enhancing their metadata and assessing FAIRness. This approach uses the Semantic Scholar API to gather data from 2013 to 2024, applying Latent Dirichlet Allocation (LDA) topic modeling for analysis. The study found that repository hosting sites and data formats impact citation patterns and usability, suggesting that improved annotation enhances dataset discoverability and reuse. AI

影响 Enhances discoverability and reuse of research artifacts, potentially accelerating AI development by improving access to software engineering data.

排序理由 This is a research paper detailing a new methodology for dataset analysis. [lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.AI 阅读 →

• Mining Software Repositories (MSR)
• MIRAGE
• Muhammad Khuram Shahzad
• Semantic Scholar API

• 论文
• 其他

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