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Alexander Quispe

Publications and source records attributed to Alexander Quispe.

3 recordsLinked to original sources

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

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.

cs.SE

Agentic Delegation and the Language Frontier of Software Developers: A Model and Evidence from Claude Code on GitHub

We develop and test a model of agentic delegation in software production. Developers face language-specific entry thresholds; conversational AI mainly augments work in languages they already know, while agentic AI adds delegated execution under developer specification and verification. The model predicts an activation band of unfamiliar languages that become feasible only with an agent, expanding the observed language-production frontier of the developer. We test this prediction in a monthly GitHub panel of 5,346 developers, dating adoption by first Claude Code co-authorship and constructing commit-level language outcomes from 57 million changed files. Doubly robust staggered-adoption event studies with not-yet-treated comparisons show sharp expansion at adoption: active languages rise by 2.5 relative to a 0.9 baseline, newly used languages by 1.2, entropy by 0.38, and cumulative breadth continues to grow afterward. The pattern survives removing the treatment-defining language, excluding all Claude-coauthored commits, conditioning on activity, and screening users of competing agents. Consistent with the model, first uses of unfamiliar languages concentrate among narrow pre-adoption specialists at each activity level. Because adoption is voluntary and may coincide with project shocks, the estimates are event-time associations rather than definitive causal effects.

econ.GN

Impact of the Availability of ChatGPT on Software Development: A Synthetic Difference in Differences Estimation using GitHub Data

Advancements in Artificial Intelligence, particularly with ChatGPT, have significantly impacted software development. Utilizing novel data from GitHub Innovation Graph, we hypothesize that ChatGPT enhances software production efficiency. Utilizing natural experiments where some governments banned ChatGPT, we employ Difference-in-Differences (DID), Synthetic Control (SC), and Synthetic Difference-in-Differences (SDID) methods to estimate its effects. Our findings indicate a significant positive impact on the number of git pushes, repositories, and unique developers per 100,000 people, particularly for high-level, general purpose, and shell scripting languages. These results suggest that AI tools like ChatGPT can substantially boost developer productivity, though further analysis is needed to address potential downsides such as low quality code and privacy concerns.

cs.SE