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

MIRAGE is a metadata-enriched framework for analyzing Mining Software Repositories (MSR) datasets from 2013-2024, incorporating FAIRness assessments and topic modeling to improve dataset discoverability and reusability. The research demonstrates that repository hosting sites and data formats significantly influence citation patterns and dataset utility in software engineering research.

The MIRAGE framework addresses a critical gap in academic research infrastructure by systematizing how software engineering datasets are cataloged, described, and discovered. Rather than treating MSR datasets as isolated artifacts, the research applies structured metadata enrichment and FAIR principles—findability, accessibility, interoperability, and reusability—creating standardized annotations that enable researchers to identify suitable datasets more effectively. This matters because reproducibility and data reuse remain persistent challenges in software engineering research, where fragmented datasets across multiple hosting platforms create friction for meta-analyses and comparative studies.

The methodology leverages Semantic Scholar API for automated metadata collection and Latent Dirichlet Allocation for topic extraction, revealing actionable patterns about which repository choices and formats maximize research impact. The paper's findings that hosting site and format selection correlate with citation frequency have practical implications for researchers publishing datasets. This connection between technical choices and downstream research value creates incentives for better infrastructure decisions at the time of dataset publication.

For the broader AI and software engineering research community, enhanced dataset discoverability accelerates the pace of secondary research and reduces duplicative data collection efforts. Tools like MIRAGE lower barriers for researchers entering new domains who might otherwise waste resources locating or reconstructing existing datasets. The work also establishes empirical baselines for dataset quality assessment, enabling future tool developers to automate FAIRness evaluation and recommendation systems.

Future developments likely include automated metadata extraction extending beyond MSR domains, integration with dataset registry platforms, and machine learning models predicting dataset citation impact based on structural attributes.

• →MIRAGE enriches MSR dataset metadata using semantic analysis and FAIR principles to improve discoverability and reusability across 2013-2024 research.
• →Repository hosting platform and data format selection measurably influence citation patterns and dataset utility in software engineering research.
• →Structured metadata annotations enable automated assessment of dataset quality, accessibility, and interoperability at scale.
• →The framework reveals topic trends in MSR research through LDA modeling, helping researchers identify emerging research areas and dataset gaps.
• →Enhanced dataset infrastructure reduces friction in research reproducibility and accelerates secondary analysis in software engineering.