arXiv:2607.05443v1 Announce Type: cross Abstract: Scientists increasingly rely on open-source tools to support their research workflows, yet discovering relevant software among over 600 million GitHub repositories remains challenging. Existing code search benchmarks focus on general software engineering tasks and fail to capture the domain-specific vocabulary and needs of scientific computing. We present a curated corpus of 5,264 high-quality, domain-classified scientific repositories spanning five NASA Science Mission Directorate divisions -- Earth Science, Astrophysics, Planetary Science, He
The proliferation of open-source tools in scientific research coupled with the massive scale of code repositories necessitates better discovery mechanisms, making advanced code search a critical current need.
Improved scientific code search can accelerate research by making existing tools more discoverable and reusable, potentially leading to faster breakthroughs and more efficient scientific workflows.
The development of domain-specific benchmarks and datasets for scientific code search will enable AI models to better understand and retrieve relevant code for specialized research tasks, moving beyond generic software engineering approaches.
- · Scientific researchers
- · AI/ML research labs
- · Open-source scientific tool developers
- · NASA Science Mission Directorate divisions
- · Researchers reliant on manual code discovery
- · Generic code search engines lacking domain specificity
Scientists gain more efficient access to relevant open-source code and tools, reducing redundant work.
Accelerated scientific discovery and increased collaboration due to easier integration and adaptation of existing research software.
The creation of new scientific fields or methodologies that were previously impractical due to the high barrier of discovering and utilizing specialized code.
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Read at arXiv cs.AI