# codefuse-ai/Awesome-Code-LLM

[TMLR] A curated list of language modeling researches for code (and other software engineering activities), plus related datasets.

Repository: https://github.com/codefuse-ai/Awesome-Code-LLM
Canonical: https://ross.abutalabs.com/products/awesome-code-llm
Homepage: https://arxiv.org/abs/2311.07989
License Family: other
Topics: datasets, llm, papers, software-engineering, survey, ai, awesome, nlp, tmlr
Last push: 2026-05-20T07:33:12+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 83, release rhythm 35, longevity 76
- inputs: {"age_days": 1077, "days_push": 105, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3432, forks 237 (observed 2026-08-28T04:08:04.173285+00:00)

## What it is
A curated awesome-list accompanying a TMLR survey on language models for code, cataloguing 70+ models, 40+ evaluation tasks, 180+ datasets, and 900+ related papers. It is regularly updated with new publications from venues like EMNLP and ICML.

## Use cases
- find research papers on code LLMs
- find datasets for training code language models
- survey of LLMs for software engineering
- literature review for code generation research
- find benchmarks for evaluating code models
- keep up with new code LLM papers

## When to choose
- you need a comprehensive, categorized bibliography of code LLM research
- you are writing a survey or literature review on language models for code
- you want curated links to code LLM datasets and benchmarks

## When to avoid
- you need runnable code or a trained model rather than paper references
- you want a tool or library rather than a reading list

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: large-language-models, tutorials, awesome-lists
- platform: -
- tags: awesome-list, code-llm, survey, papers, datasets, research-curation, software-engineering, natural-language-processing, web-server

## Member repositories
- codefuse-ai/Awesome-Code-LLM (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:04.173285+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:37:45.092533+00:00, confidence not recorded.
  - readme: https://github.com/codefuse-ai/Awesome-Code-LLM (fetched 2026-08-28T04:08:04.173285+00:00, sha a1750f5bf71e)
  - homepage: https://arxiv.org/abs/2311.07989 (fetched 2026-08-29T09:32:18.078871+00:00, sha d3ea24ce1c70)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T09:32:18.088169+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T09:32:18.092168+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T09:32:18.094197+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T09:32:18.090227+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.