CORTEXA
← Browse
arxivcs.SEcs.AIcs.DB2026-07-07

Industry Classification of GitHub Repositories Using the North American Industry Classification System (NAICS)

Kevin Xu, Alexander Quispe

GitHub hosts hundreds of millions of public repositories, but the platform exposes no native mapping from repositories to standardized industry sectors. This gap limits empirical work on the geography of innovation, the industrial composition of open-source production, and the diffusion of new technologies across economic sectors. We present NAICS-GH, a publicly released corpus of 6,588 GitHub repositories drawn from source pools covering the United States, the European Union, and Australia, each labeled with a 2-digit sector from the North American Industry Classification System (NAICS 2022). Labels are produced by a retrieve-and-verify pipeline that combines BAAI/bge-large-en embeddings, FAISS retrieval, and GPT-4.1 rubric scoring. The pipeline narrows about 1.37 million source repositories to 31,178 candidate repository-sector pairs and retains 6,588 high-confidence labels with score at least 8. Re-running the retrieval pipeline end to end reproduces the candidate set to within 0.03 percent. On a 2,421-repository human-validated random sample, the released labels attain 96.98 percent precision, with Wilson 95 percent confidence interval [96.23, 97.59]. We benchmark six pretrained encoders on the released corpus; RoBERTa-large reaches 86.45 percent F1 and 86.35 percent accuracy on a held-out 20 percent test set. The dataset, Croissant metadata, pipeline code, prompts, and fine-tuned checkpoint are released under CC-BY-4.0 and MIT licenses.

View free PDFSource page

Related papers

arxivcs.SEcs.AIcs.DB2026-06-28

SAKE: Software Architectural Knowledge Evaluation Benchmark for Large Language Models

Tiziano Santilli, Francesco Daghero, Mayhar Tourchi Moghaddam

Large Language Models (LLMs) are increasingly used as assistants across the software development lifecycle, yet their ability to reason about software architecture remains largely unmeasured. Architectural decision-making depends on quality attribute trade-offs, design patterns,…

View free PDFSource page
arxivcs.SEcs.AIcs.DB2026-07-01

Knowledge-Centric Information Systems

Mariano Garralda-Barrio

For decades, data engineering has developed mature architectural principles for integrating, governing, validating, cataloging, and serving organizational data. The rise of large language models does not eliminate these concerns; it exposes a broader version of them. Organization…

View free PDFSource page
arxivcs.SEcs.AI2026-07-01

A Methodology for Investigating AI Patterns Prevalence in Software Repositories

Srinath Perera, Hasinthaka Piyumal, Frank Leymann, Rania Khalaf

As Artificial Intelligence(AI)-based applications take off, a clear understanding of AI patterns can uplift the quality of AI applications. Many AI patterns have been proposed in the literature; however, their prevalence in real-life code has not yet been validated. Understanding…

View free PDFSource page
arxivcs.SEcs.AI2026-07-17

When to Use Which? Benchmarking Optimisers for Configurable Systems under Varying Budgets

Chao Jiang, Yulong Ye, Tao Chen, Miqing Li

Software configuration tuning is crucial for optimising system performance, and various optimisers have emerged over the last decade. Yet, the time required during the tuning process may vary across systems. In some systems (e.g., PostgreSQL), it may take a few minutes to measure…

View free PDFSource page
arxivcs.SEcs.AI2026-07-06

Three-Phase Evaluation of AI-Assisted Software Development Life Cycle

Joshua Strubel, Professor Carrie Russell, Carson Crockett, Jason Ferraro, Nathan Londhe, Uzayr Syed, et al.

This paper presents an exploratory evaluation of how increasing levels of AI autonomy affect software development productivity, requirement adherence, and developer cognitive workload. A team of four developers reimplemented the same full-stack web application across three sequen…

View free PDFSource page
arxivcs.SEcs.AI2026-07-02

An Exploratory Study on LLM-Generated Code and Comments in Code Repositories

Yongyi Ji, Jiaji Wang, Yi Zhou, Fuxiang Chen, Hongji Yang

The use of LLMs in software development has become increasingly widespread on tasks such as code generation and summarization. Reports from large technology companies showed that around 20% to 30% of their code are generated by LLMs. However, there remains skepticism about the pr…

View free PDFSource page