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A Taxonomy of Programming Languages for Code Generation

arXiv 2604.00239 ↗First posted 31 Mar 2026cs.CLcited by 0

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Abstract (arXiv)

The world's 7,000+ languages vary widely in the availability of resources for NLP, motivating efforts to systematically categorize them by their degree of resourcefulness (Joshi et al., 2020). A similar disparity exists among programming languages (PLs); however, no resource-tier taxonomy has been established for code. As large language models (LLMs) grow increasingly capable of generating code, such a taxonomy becomes essential. To fill this gap, we present the first reproducible PL resource classification, grouping 646 languages into four tiers. We show that only 1.9% of languages (Tier 3, High) account for 74.6% of all tokens in seven major corpora, while 71.7% of languages (Tier 0, Scarce) contribute just 1.0%. Statistical analyses of within-tier inequality, dispersion, and distributional skew confirm that this imbalance is both extreme and systematic. Our results provide a principled framework for dataset curation and tier-aware evaluation of multilingual LLMs.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 35 references that are in the map. First posted on arXiv 31 Mar 2026.

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References · 22

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  3. 2023Code Llama: Open Foundation Models for Codecited by 2,105
  4. 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,734
  5. 2022Competition-Level Code Generation with AlphaCodecited by 1,427
  6. 2024DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligencecited by 1,154
  7. 2023StarCoder: may the source be with you!cited by 790
  8. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 789
  9. 2019CodeSearchNet Challenge: Evaluating the State of Semantic Code Searchcited by 638
  10. 2024StarCoder 2 and The Stack v2: The Next Generationcited by 484
  11. 2021Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributionscited by 417
  12. 2022A Systematic Evaluation of Large Language Models of Codecited by 360
10 more
  1. 2022The Stack: 3 TB of permissively licensed source codecited by 354
  2. 2023CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-Xcited by 294
  3. 2021CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Taskscited by 199
  4. 2023Large Language Models for Software Engineering: Survey and Open Problemscited by 178
  5. 2022Language Models of Code are Few-Shot Commonsense Learnerscited by 160
  6. 2022Multi-lingual Evaluation of Code Generation Modelscited by 158
  7. 2023CodeGen2: Lessons for Training LLMs on Programming and Natural Languagescited by 124
  8. 2024Do Current Language Models Support Code Intelligence for R Programming Language?cited by 4
  9. 2025The Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Modelscited by 4
  10. 2024Code LLMs: A Taxonomy-based Surveycited by 3

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