MIRAGE: Metadata-Integrated Repository Analysis and Guided Enhancement for MSR Datasets

2026-05-29 · Source: Machine Learning · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

MIRAGE proposes an improved approach for analyzing Mining Software Repositories (MSR) datasets through metadata enrichment, FAIRness assessment, and topic-driven analysis. This research expands an existing MSR dataset directory by adding new annotations, enriching metadata categories, and offering advanced filtering options. Metadata from MSR papers published between 2013 and 2024 was collected using the Semantic Scholar API. The analysis employs Latent Dirichlet Allocation (LDA) topic modeling and statistical methods. Key dataset-level attributes, including repository hosting site, format, accessibility, reusability, and dataset quality, were integrated. The study reveals that repository hosting site and data format choices significantly influence citation patterns and dataset usability. This enhanced annotation method ultimately improves MSR dataset analysis and discoverability, fostering more effective reuse and evaluation of research artifacts.

Key takeaway

For research scientists analyzing Mining Software Repositories (MSR) datasets, MIRAGE provides a critical framework to enhance data discoverability and reuse. You should consider integrating metadata enrichment, FAIRness assessment, and topic-driven analysis into your dataset management practices. This approach, which accounts for factors like hosting site and data format, will improve the utility and citation potential of your research artifacts, leading to more effective evaluation and broader impact.

Key insights

MIRAGE enhances MSR dataset analysis and discoverability through metadata enrichment, FAIRness assessment, and topic modeling.

Principles

• Metadata enrichment improves dataset analysis.
• Hosting site and data format impact dataset usability.
• Topic modeling aids dataset discoverability.

Method

Gather MSR paper metadata (2013-2024) via Semantic Scholar API, apply LDA topic modeling and statistical analysis, then enrich dataset directory attributes.

In practice

• Annotate MSR datasets with hosting, format, quality.
• Use LDA for topic-driven dataset analysis.
• Assess FAIRness to improve dataset reuse.

Topics

• Mining Software Repositories
• Metadata Enrichment
• FAIRness Assessment
• Topic Modeling
• Latent Dirichlet Allocation
• Dataset Discoverability

Best for: AI Scientist, Research Scientist, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.