MIRAGE: New Framework for Analyzing and Improving Mining Software Repositories Datasets
Researchers have introduced MIRAGE, a system for improving the analysis and discoverability of Mining Software Repositories (MSR) datasets through metadata enrichment, FAIRness assessment, and topic modeling. The work extends an earlier MSR dataset directory by adding new annotations, expanded metadata categories, and advanced filtering, drawing on papers published from 2013 to 2024 via the Semantic Scholar API. The framework aims to make research datasets more reusable and evaluable, with findings suggesting that repository hosting choices and data formats meaningfully affect citation patterns and usability.
The MIRAGE paper, submitted to arXiv in May 2026, presents an enhanced approach to cataloguing and analyzing datasets used in Mining Software Repositories (MSR) research. Building on a pre-existing dataset directory, the authors added new metadata annotations covering repository hosting site, data format, accessibility, reusability, and dataset quality. Metadata for MSR papers spanning 2013 to 2024 was collected using the Semantic Scholar API, and analysis was conducted using Latent Dirichlet Allocation (LDA) topic modeling alongside statistical methods. Key findings indicate that the choice of hosting platform and file format correlates with differences in citation rates and how easily datasets can be reused by other researchers. The enriched annotation scheme is intended to improve both the discoverability of MSR datasets and the ability of the research community to evaluate and build upon existing research artifacts. The paper spans 8 pages and includes 8 figures, and is cross-listed across Information Retrieval, Artificial Intelligence, Machine Learning, and Software Engineering on arXiv.
What's missing
The paper does not appear to report on inter-rater reliability or validation of the manual annotation process, leaving open questions about the consistency and reproducibility of the metadata enrichment. It is also unclear whether the FAIRness assessment uses an established, standardized rubric or one developed by the authors, which affects how comparable results would be across studies. The scope is limited to MSR conference papers and may not generalize to datasets published in journals or other venues.
What different sources said
- arXiv cs.AICenter
MIRAGE: Metadata-Integrated Repository Analysis and Guided Enhancement for MSR Datasets
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