arXiv Machine Learning By Syrym Abdikhan, Mazhar Hameed

PandasCorpus: A Resource of Real-World Pandas Workflows and Usage Patterns

Read the original on arXiv Machine Learning →

arXiv:2608. 14742v1 Announce Type: cross Abstract: Pandas has emerged as the de facto library for data processing and machine learning, widely used for tasks, such as data loading, transformation, and analysis.

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arXiv AI
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SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?

arXiv:2511. 06090v3 Announce Type: replace-cross Abstract: Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correctness.

By Jeffrey Jian Ma, Milad Hashemi, Amir Yazdanbakhsh, Kevin Swersky, Ofir Press, Enhui Li, Vijay Janapa Reddi, Parthasarathy Ranganathan
arXiv AI
Sep 3

SpecMine: A Large-Scale Corpus of Spec-Driven Development Artifacts

SpecMine is a large-scale corpus that documents Spec-Driven Development (SDD) artifacts in public GitHub repositories. It includes a broad census of 470,795 spec files from 73,030 repositories linked to 17 tools, a focused census of 98,574 Kiro layout files from 12,910 repositories, and a sweep of 5,992 pull requests across 581 repositories that modify specs. The dataset provides enriched metadata, full commit histories, parsed document structures, and over 2.4 million typed references connecting specs to code, sibling documents, PRs, branches, and issues.

By Shyam Agarwal, Anmol Singhal, Travis Breaux, Bogdan Vasilescu