RRepoGEO

REPOGEO REPORT · LITE

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

Default branch master · commit ffe96369 · scanned 6/21/2026, 9:37:38 AM

GitHub: 6,596 stars · 832 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface src-d/awesome-machine-learning-on-source-code, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Rephrase the 'unmaintained' notice to clarify its archival value

    Why:

    CURRENT
    Notice: This repository is no longer actively maintained, and no further updates will be done, nor issues/PRs will be answered or attended.
An alternative actively maintained can be found at ml4code.github.io repository.
    COPY-PASTE FIX
    This repository serves as a historical archive of research papers, datasets, and software projects related to Machine Learning on Source Code (MLonCode). While no longer actively maintained, its contents remain a valuable reference. For actively maintained resources, please visit ml4code.github.io.
  • mediumhomepage#2
    Add the alternative project's URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://ml4code.github.io
  • lowtopics#3
    Add specific topics to clarify its archival nature

    Why:

    CURRENT
    awesome, awesome-list, machine-learning, machine-learning-on-source-code
    COPY-PASTE FIX
    awesome, awesome-list, machine-learning, machine-learning-on-source-code, archive, historical-reference

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface src-d/awesome-machine-learning-on-source-code
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Awesome-Code-Machine-Learning
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome-Code-Machine-Learning · recommended 1×
  2. Papers With Code · recommended 1×
  3. GitHub · recommended 1×
  4. Google Scholar · recommended 1×
  5. Zenodo · recommended 1×
  • CATEGORY QUERY
    Where can I find research papers and datasets for machine learning on source code?
    you: not recommended
    AI recommended (in order):
    1. Awesome-Code-Machine-Learning
    2. Papers With Code
    3. GitHub
    4. Google Scholar
    5. Zenodo
    6. Figshare
    7. Kaggle
    8. Microsoft Research Open Data
    9. Google Datasets Search

    AI recommended 9 alternatives but never named src-d/awesome-machine-learning-on-source-code. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best approaches for applying AI to program analysis and code generation?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4
    2. Google Gemini
    3. Meta Code Llama
    4. GitHub Copilot
    5. PyTorch
    6. TensorFlow
    7. CodeBERT
    8. GraphCodeBERT
    9. Rosette
    10. Z3
    11. Sketch
    12. Microsoft PROSE
    13. OpenAI Gym
    14. Stable Baselines3
    15. RLlib
    16. SonarQube
    17. ESLint

    AI recommended 17 alternatives but never named src-d/awesome-machine-learning-on-source-code. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of src-d/awesome-machine-learning-on-source-code?
    pass
    AI named src-d/awesome-machine-learning-on-source-code explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts src-d/awesome-machine-learning-on-source-code in production, what risks or prerequisites should they evaluate first?
    pass
    AI named src-d/awesome-machine-learning-on-source-code explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo src-d/awesome-machine-learning-on-source-code solve, and who is the primary audience?
    pass
    AI did not name src-d/awesome-machine-learning-on-source-code — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)
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src-d/awesome-machine-learning-on-source-code — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite