# src-d/awesome-machine-learning-on-source-code

Cool links & research papers related to Machine Learning applied to source code (MLonCode)

Repository: https://github.com/src-d/awesome-machine-learning-on-source-code
Canonical: https://ross.abutalabs.com/products/awesome-machine-learning-on-source-code
License: CC-BY-SA-4.0
License Family: other
Topics: awesome-list, awesome, machine-learning, machine-learning-on-source-code
Last push: 2020-12-03T17:58:42+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3361, "days_push": 2099, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6634, forks 830 (observed 2026-08-28T04:09:45.844894+00:00)

## What it is
A curated awesome-list of research papers, datasets, talks, and software projects on applying machine learning to source code (MLonCode). It is explicitly no longer maintained, with an actively maintained successor at ml4code.github.io.

## Use cases
- find research papers on machine learning for source code
- learn about code completion and code suggestion models
- find datasets for training models on source code
- explore program synthesis and program repair research
- get started with MLonCode research
- find papers on code clone detection and bug detection

## When to choose
- you need a broad starting point for MLonCode research papers and datasets
- you want curated links covering code embeddings, program translation, and code summarization

## When to avoid
- you need up-to-date coverage of recent research, since the list is unmaintained
- you want actively maintained tooling rather than a link collection

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: machine-learning, documentation, developer-tools
- domain: machine-learning, awesome-lists, developer-tools, tutorials
- platform: cross-platform
- tags: awesome-list, mloncode, research-papers, source-code-analysis, curated-list

## Member repositories
- src-d/awesome-machine-learning-on-source-code (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:45.844894+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 2026-08-29T17:43:13.172374+00:00, confidence not recorded.
  - readme: https://github.com/src-d/awesome-machine-learning-on-source-code (fetched 2026-08-28T04:09:45.844894+00:00, sha ae1173bacf14)
- Data as of 2026-08-30T08:39:29.467469+00:00.