README.md

---
license: gpl-3.0
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    dtype: int64
  - name: language
    dtype: string
  - name: extension
    dtype: string
  - name: total_lines
    dtype: int64
  - name: avg_line_length
    dtype: float64
  - name: max_line_length
    dtype: int64
  - name: alphanum_fraction
    dtype: float64
  - name: repo_name
    dtype: string
  - name: repo_stars
    dtype: int64
  - name: repo_forks
    dtype: int64
  - name: repo_open_issues
    dtype: int64
  - name: repo_license
    dtype: string
  - name: repo_extraction_date
    dtype: string
  - name: exact_duplicates_stackv2
    dtype: bool
  - name: exact_duplicates_stackv1
    dtype: bool
  - name: near_duplicates_stackv1
    dtype: bool
  - name: near_duplicates_stackv2
    dtype: bool
  splits:
  - name: train
    num_bytes: 39780040
    num_examples: 3896
  download_size: 18918820
  dataset_size: 39780040
- config_name: Starlark
  features:
  - name: id
    dtype: int64
  - name: file_name
    dtype: string
  - name: file_path
    dtype: string
  - name: content
    dtype: string
  - name: size
    dtype: int64
  - name: language
    dtype: string
  - name: extension
    dtype: string
  - name: total_lines
    dtype: int64
  - name: avg_line_length
    dtype: float64
  - name: max_line_length
    dtype: int64
  - name: alphanum_fraction
    dtype: float64
  - name: repo_name
    dtype: string
  - name: repo_stars
    dtype: int64
  - name: repo_forks
    dtype: int64
  - name: repo_open_issues
    dtype: int64
  - name: repo_license
    dtype: string
  - name: repo_extraction_date
    dtype: string
  - name: exact_duplicates_stackv2
    dtype: bool
  - name: near_duplicates_stackv2
    dtype: bool
  splits:
  - name: train
    num_bytes: 3415162
    num_examples: 487
  download_size: 1304191
  dataset_size: 3415162
- config_name: Swift
  features:
  - name: id
    dtype: int64
  - name: file_name
    dtype: string
  - name: file_path
    dtype: string
  - name: content
    dtype: string
  - name: size
    dtype: int64
  - name: language
    dtype: string
  - name: extension
    dtype: string
  - name: total_lines
    dtype: int64
  - name: avg_line_length
    dtype: float64
  - name: max_line_length
    dtype: int64
  - name: alphanum_fraction
    dtype: float64
  - name: repo_name
    dtype: string
  - name: repo_stars
    dtype: int64
  - name: repo_forks
    dtype: int64
  - name: repo_open_issues
    dtype: int64
  - name: repo_license
    dtype: string
  - name: repo_extraction_date
    dtype: string
  - name: exact_duplicates_stackv2
    dtype: bool
  - name: exact_duplicates_stackv1
    dtype: bool
  - name: near_duplicates_stackv1
    dtype: bool
  - name: near_duplicates_stackv2
    dtype: bool
  splits:
  - name: train
    num_bytes: 2209604083
    num_examples: 434849
  download_size: 617813505
  dataset_size: 2209604083
- config_name: Vue
  features:
  - name: id
    dtype: int64
  - name: file_name
    dtype: string
  - name: file_path
    dtype: string
  - name: content
    dtype: string
  - name: size
    dtype: int64
  - name: language
    dtype: string
  - name: extension
    dtype: string
  - name: total_lines
    dtype: int64
  - name: avg_line_length
    dtype: float64
  - name: max_line_length
    dtype: int64
  - name: alphanum_fraction
    dtype: float64
  - name: repo_name
    dtype: string
  - name: repo_stars
    dtype: int64
  - name: repo_forks
    dtype: int64
  - name: repo_open_issues
    dtype: int64
  - name: repo_license
    dtype: string
  - name: repo_extraction_date
    dtype: string
  - name: exact_duplicates_stackv2
    dtype: bool
  - name: exact_duplicates_stackv1
    dtype: bool
  - name: near_duplicates_stackv1
    dtype: bool
  - name: near_duplicates_stackv2
    dtype: bool
  splits:
  - name: train
    num_bytes: 1825065032
    num_examples: 321502
  download_size: 556310936
  dataset_size: 1825065032
- config_name: WebAssembly
  features:
  - name: id
    dtype: int64
  - name: file_name
    dtype: string
  - name: file_path
    dtype: string
  - name: content
    dtype: string
  - name: size
    dtype: int64
  - name: language
    dtype: string
  - name: extension
    dtype: string
  - name: total_lines
    dtype: int64
  - name: avg_line_length
    dtype: float64
  - name: max_line_length
    dtype: int64
  - name: alphanum_fraction
    dtype: float64
  - name: repo_name
    dtype: string
  - name: repo_stars
    dtype: int64
  - name: repo_forks
    dtype: int64
  - name: repo_open_issues
    dtype: int64
  - name: repo_license
    dtype: string
  - name: repo_extraction_date
    dtype: string
  - name: exact_duplicates_stackv2
    dtype: bool
  - name: exact_duplicates_stackv1
    dtype: bool
  - name: near_duplicates_stackv2
    dtype: bool
  - name: near_duplicates_stackv1
    dtype: bool
  splits:
  - name: train
    num_bytes: 74028223
    num_examples: 544
  download_size: 21059883
  dataset_size: 74028223
configs:
- config_name: ANTLR
  data_files:
  - split: train
    path: data/ANTLR/train-*
- config_name: Ada
  data_files:
  - split: train
    path: data/Ada/train-*
- config_name: Agda
  data_files:
  - split: train
    path: data/Agda/train-*
- config_name: Apex
  data_files:
  - split: train
    path: data/Apex/train-*
- config_name: Assembly
  data_files:
  - split: train
    path: data/Assembly/train-*
- config_name: C
  data_files:
  - split: train
    path: data/C/train-*
- config_name: C#
  data_files:
  - split: train
    path: data/C#/train-*
- config_name: C++
  data_files:
  - split: train
    path: data/C++/train-*
- config_name: COBOL
  data_files:
  - split: train
    path: data/COBOL/train-*
- config_name: Clojure
  data_files:
  - split: train
    path: data/Clojure/train-*
- config_name: Common Lisp
  data_files:
  - split: train
    path: data/Common Lisp/train-*
- config_name: Coq
  data_files:
  - split: train
    path: data/Coq/train-*
- config_name: Crystal
  data_files:
  - split: train
    path: data/Crystal/train-*
- config_name: Cuda
  data_files:
  - split: train
    path: data/Cuda/train-*
- config_name: D
  data_files:
  - split: train
    path: data/D/train-*
- config_name: Dart
  data_files:
  - split: train
    path: data/Dart/train-*
- config_name: EJS
  data_files:
  - split: train
    path: data/EJS/train-*
- config_name: Elixir
  data_files:
  - split: train
    path: data/Elixir/train-*
- config_name: Emacs Lisp
  data_files:
  - split: train
    path: data/Emacs Lisp/train-*
- config_name: Erlang
  data_files:
  - split: train
    path: data/Erlang/train-*
- config_name: F#
  data_files:
  - split: train
    path: data/F#/train-*
- config_name: Forth
  data_files:
  - split: train
    path: data/Forth/train-*
- config_name: Go
  data_files:
  - split: train
    path: data/Go/train-*
- config_name: Groovy
  data_files:
  - split: train
    path: data/Groovy/train-*
- config_name: Hack
  data_files:
  - split: train
    path: data/Hack/train-*
- config_name: Haskell
  data_files:
  - split: train
    path: data/Haskell/train-*
- config_name: Java
  data_files:
  - split: train
    path: data/Java/train-*
- config_name: JavaScript
  data_files:
  - split: train
    path: data/JavaScript/train-*
- config_name: Julia
  data_files:
  - split: train
    path: data/Julia/train-*
- config_name: Kotlin
  data_files:
  - split: train
    path: data/Kotlin/train-*
- config_name: Less
  data_files:
  - split: train
    path: data/Less/train-*
- config_name: Lua
  data_files:
  - split: train
    path: data/Lua/train-*
- config_name: Mathematica
  data_files:
  - split: train
    path: data/Mathematica/train-*
- config_name: Matlab
  data_files:
  - split: train
    path: data/Matlab/train-*
- config_name: NetLogo
  data_files:
  - split: train
    path: data/NetLogo/train-*
- config_name: NewLisp
  data_files:
  - split: train
    path: data/NewLisp/train-*
- config_name: Nix
  data_files:
  - split: train
    path: data/Nix/train-*
- config_name: OCaml
  data_files:
  - split: train
    path: data/OCaml/train-*
- config_name: Objective-C
  data_files:
  - split: train
    path: data/Objective-C/train-*
- config_name: PHP
  data_files:
  - split: train
    path: data/PHP/train-*
- config_name: Pascal
  data_files:
  - split: train
    path: data/Pascal/train-*
- config_name: Perl
  data_files:
  - split: train
    path: data/Perl/train-*
- config_name: Processing
  data_files:
  - split: train
    path: data/Processing/train-*
- config_name: Prolog
  data_files:
  - split: train
    path: data/Prolog/train-*
- config_name: Python
  data_files:
  - split: train
    path: data/Python/train-*
- config_name: R
  data_files:
  - split: train
    path: data/R/train-*
- config_name: Raku
  data_files:
  - split: train
    path: data/Raku/train-*
- config_name: Ruby
  data_files:
  - split: train
    path: data/Ruby/train-*
- config_name: Rust
  data_files:
  - split: train
    path: data/Rust/train-*
- config_name: SQL
  data_files:
  - split: train
    path: data/SQL/train-*
- config_name: Scala
  data_files:
  - split: train
    path: data/Scala/train-*
- config_name: Scheme
  data_files:
  - split: train
    path: data/Scheme/train-*
- config_name: Scilab
  data_files:
  - split: train
    path: data/Scilab/train-*
- config_name: Starlark
  data_files:
  - split: train
    path: data/Starlark/train-*
- config_name: Swift
  data_files:
  - split: train
    path: data/Swift/train-*
- config_name: Vue
  data_files:
  - split: train
    path: data/Vue/train-*
- config_name: WebAssembly
  data_files:
  - split: train
    path: data/WebAssembly/train-*
---
# The Heap Dataset

We develop **The Heap**, a new contamination-free multilingual code dataset comprising 57 languages, which facilitates LLM evaluation reproducibility. The reproduction packge can be found [here](https://anonymous.4open.science/r/heap-forge/README.md).

# Is your code in The Heap? 

 If you would like to have your data removed from the dataset, follow the instructions on [GitHub](https://github.com/AISE-TUDelft/The-Heap-opt-out).

# Citation
If you use this dataset as part of your research please cite us:

```bibtex
@INPROCEEDINGS {11052803,
author = { Katzy, Jonathan and Popescu, Razvan Mihai and van Deursen, Arie and Izadi, Maliheh },
booktitle = { 2025 IEEE/ACM Second International Conference on AI Foundation Models and Software Engineering (Forge) },
title = {{ The Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Models }},
year = {2025},
pages = {151-155},
doi = {10.1109/Forge66646.2025.00025},
url = {https://doi.ieeecomputersociety.org/10.1109/Forge66646.2025.00025},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month =apr}
```

# Usage 

Using the Datasets API, our dataset can be used as follows:

```python
from datasets import load_dataset

dataset_name = 'redpajama'
language = 'Python'

ds = load_dataset(
    "AISE-TUDelft/the-heap",
    f"{language}",
    split="train",
    num_proc=16
)

ds = ds.filter(lambda x: not x[f'exact_duplicates_{dataset_name}'] and not x[f'near_duplicates_{dataset_name}'])

```

# Collection

We collect up to **50,000** public repositories using the [GitHub API](https://docs.github.com/en/rest/search/search?apiVersion=2022-11-28), focusing on *license type*, *star count*, and *creation date*. Repositories with non-permissive licenses are prioritized to reduce contamination, as public code datasets we deduplicate against primarily focus on permissive or no-license repositories. We select repositories created before **August 2024** in decreasing order of their star counts. To handle GitHub rate limits, we use timeouts and pagination during the scraping process.

### Copyleft licenses included in the The Heap
| **License**    | **Family** |
| :--------: | :-------: |
| CECILL-1.0, CECILL-1.1, CECILL-2.0, <br> CECILL-2.1,  CECILL-C, EPL-1.0, EPL-2.0, <br> LGPL-2.1, LGPL-3.0, MS-RL, MPL-2.0     | Weak Copyleft |
| GPL-2.0, GPL-3.0 | Strong Copyleft   |
| AGPL-3.0, EUPL-1.1, EUPL-1.2, OSL-3.0 | Network Copyleft   |


The features we extract for each repository are illustrated in the example below.

```json
  {
    "id": 126178683,
    "full_name": "halo-dev/halo",
    "html_url": "https://github.com/halo-dev/halo",
    "stargazers_count": 29115,
    "forks_count": 8985,
    "watchers_count": 29115,
    "open_issues_count": 278,
    "language": "Java",
    "created_at": "2018-03-21T12:56:52Z",
    "pushed_at": "2023-10-28T16:29:39Z",
    "license": {
      "key": "gpl-3.0",
      "name": "GNU General Public License v3.0",
      "spdx_id": "GPL-3.0",
      "url": "https://api.github.com/licenses/gpl-3.0",
      "node_id": "MDc6TGljZW5zZTk="
    },
    "retrieval_date": "10/30/2023, 3:24:57 PM (Europe/Amsterdam)"
  }

```

### Repository Fields

- **id**: unique id of the repo
- **full_name**: complete name of the repo
- **html_url**: URL to the repo
- **stargazers_count**: number of stars of the repo
- **forks_count**: number of forks of the repo
- **watchers_count**: number of watchers of the repo
- **open_issues_count**: number of open issues of the repo at the extraction time
- **language**: main language of the repo
- **created_at**: creation date of the repo
- **pushed_at**: date of the most recent push to the repo until the extraction date
- **license**: license type of the repo
- **retrieval_date**: date when the repo was scraped from GitHub

We start by retrieving repositories with more than **900** stars using **two-month tumbling windows**. If we hit the **1000** repository limit per window (for a personal GitHub account), we shorten the
search space to a **one-month window** and restart the iteration. Otherwise, the window advances by two months. Once the entire timeframe (until **August 2024**) is covered, we reduce the star search space: between **900** and **100** stars, we decrease the interval by **50** (e.g. search between [900, 850]), between **100** and **10** stars, we decrease the interval by **10**, and for the last **10** stars, we decrease by **1**. Since most repositories fall within the **0-100 star range** (e.g. Figure 1 showcases the distribution of repositories with up to **500** stars for Java), using the **creation date** and **star count** filters helps us avoid API limits and scrape more data by narrowing the search space. 
The creation date window can be reduced even further (week or day level), in order to extract more data. We remove any potential duplicated repositories obtained due to the pagination process. Lastly, we extract all the files corresponding to each language. We extend the programming languages extension list used for [The Stack](https://gist.github.com/ppisarczyk/43962d06686722d26d176fad46879d41) with 4 languages: EJS, Raku, Starlark, and WebAssembly. 


<!-- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66a89f0fd6625ead0411af50/fctcChY0DRwxMeXazUWUV.png) -->
<div style="text-align: center;">
    <img src=https://cdn-uploads.huggingface.co/production/uploads/66a89f0fd6625ead0411af50/fctcChY0DRwxMeXazUWUV.png alt="Figure 1: Distribution of scraped repositories with at most 500 stars." style="display: block; margin: 0 auto; width: 600px; height: auto;" />
    <p><b>Figure 1:</b> Distribution of scraped repositories with at most 500 stars for Java</p>
</div>


# Cleaning

The next stage in our dataset pipeline is the cleaning procedure. We exclude any files **larger than 10 MB** and those with **fewer than 10 words**.

# Deduplication

The final stage of our dataset pipeline is the deduplication process. We apply both exact and near deduplication against open code datasets listed in the table below. 

### Open code datasets used for deduplication
| **Dataset**    | **Source** |
| :--------: | :-------: |
| The Stack V2  | All permissively licensed and unlicensed files collected in the Software Heritage archive.   |
| The Stack   | All permissively licensed repositories collected in the GHArchive and scraped from GitHub.    |
| Red Pajama |    Repositories from the GitHub dataset hosted by Google BigQuery licensed under MIT, BSD, or Apache licenses.    |
| GitHub Code |    Repositories from the GitHub dataset hosted by Google BigQuery.    |
| CodeParrot | All Python files from the GitHub dataset hosted by Google BigQuery. |

## Exact Deduplication
We remove exact duplicates **within our dataset itself**, and then we apply exact deduplication against the open datasets. For that, we use the **sha256** function to generate hashes for each file.
We choose this hash function because it provides a uniform distribution of hash values, minimizes collisions, and ensures even distribution across the hash space.

## Near Deduplication

We apply the **MinHashLSH** algorithm using the [*datasketch1*](https://ekzhu.com/datasketch/lsh.html) library. To calculate the minhashes, we use the same hash function as above, but we extract the first 16 bytes to generate 128-bit hash values. 
This approach balances the need for a strong hash function with the efficiency of a shorter hash length. 

Additionally, we use **128** file permutations for LSH, with weights of **0.4** for **precision** and **0.6** for **recall**. We generate **7-character shingles** after [lowercasing the file content and removing whitespace](http://infolab.stanford.edu/~ullman/mmds/book.pdf). 
We find that 7-shingles provide a reasonable trade-off between the number of shingles and the data processed, being small enough to keep the number of unique shingles manageable yet large enough to provide meaningful comparisons. 
It was shown that the number of shingles should be large enough to ensure a low probability of shingles appearing across documents, with **k = 5** suggested for smaller documents such as [emails](http://infolab.stanford.edu/~ullman/mmds/book.pdf). 
However, code files usually contain a **larger dictionary** of characters than emails, including arithmetic and comparison operators which are less frequent in emails. 
Thus, given the increased **complexity** and **size** of code files, we consider 7-shingles to be appropriate to capture sufficient context, ensuring uniqueness and **reducing false positives**, which smaller shingles such as k = 5 might fail to achieve. 
Furthermore, **k = 9** was shown to be a safe choice for [large research articles](http://infolab.stanford.edu/~ullman/mmds/book.pdf), however, for our needs, 7-shingles strike a balance between accuracy and
computational efficiency, crucial for handling the extensive size of the datasets. This choice provides better computational efficiency by reducing the number of comparisons while maintaining a manageable shingle space.
Lastly, we use a **Jaccard similarity threshold** of **0.7**, which proved to be efficient for both [SantaCoder](https://arxiv.org/abs/2301.03988) and [StarCoder](https://arxiv.org/abs/2305.06161) models. A high threshold
reduces false positives, leading to fewer unnecessary comparisons and lower computational overhead. Moreover, this standard threshold value has been shown to be [robust for duplicate detection](https://dl.acm.org/doi/10.1145/3359591.3359735). 


Instead of removing exact and near duplicates found against other open datasets, we add a boolean mask to our dataset. This approach enhances reproducibility by allowing researchers to filter the dataset for unique files, according to their specific requirements.

The final dataset structure is shown in the example below.


```json
{
    "id": 538,
    "file_name": "Font.java",
    "file_path": ".../lateralgm/resources/Font.java",
    "content": "*/ package org.lateralgm.resources; import java.util.EnumMap; import org.lateralgm.main.Prefs; ...",
    "size": 1,985,
    "language": "Java",
    "extension": ".java",
    "total_lines": 3,835,
    "avg_line_length": 29,834,
    "max_line_length": 567,
    "alphanum_fraction": 0,645,
    "repo_name": "lwizchz/GameMaker-HTML5-Player",
    "repo_stars": 22,
    "repo_forks": 9,
    "repo_open_issues": 0,
    "repo_license": "GPL-3.0",
    "repo_extraction_date": "9/19/2024, 2:48:21 PM (Europe/Amsterdam)"
    "exact_duplicates_stackv1": False,
    "exact_duplicates_stackv2": True,
    "near_duplicates_stackv1": True,
    "near_duplicates_stackv2": False,
     ....
 
  } 

```

### Dataset Fields

- **id**: unique ID of the file
- **file_name**: name of the file extracted from its repo
- **file_path**: path to the file in its repo
- **content**: content of the file
- **size**: size of the file
- **language**: language of the file
- **total_lines**: number of total lines of the file
- **avg_line_length**: average line length of the file
- **max_line_length**: max line length of the file
- **alphanum_fraction**: alphanumeric fraction of the file
- **extension**: language extension of the file
- **repo_name**: complete name of the file's repo
- **repo_stars**: number of stars of the file's repo
- **repo_forks**: number of forks of the file's repo
- **repo_open_issues**: number of open issues of the file's repo at the extraction date
- **repo_license**: license of the file's repo
- **repo_extraction_date**: extraction date of file's repo
- **exact_duplicates_pubdataset**: boolean flag stating if there are any exact duplicate files found against another public dataset (The Stackv2, The Stack, RedPajama, GithubCode, CodeParrot)
- **near_duplicates_pubdataset**: boolean flag stating if there are any near duplicate files found against another public dataset (The Stackv2, The Stack, RedPajama, GithubCode, CodeParrot)

The distribution of the languages in The Heap is presented in the table below. The third column shows the number of files collected after filtering based on file size and word count. The last column indicates the number of files remaining after removing exact duplicates within the dataset, with exact and near duplicates compared to other datasets flagged among the remaining files.

### Programming languages included in The Heap
| **Language** | **Repositories** | **Raw Files** | **Unique Files** |
| --- | --- | --- | --- |
| Ada | 676 | 41,367 | 34,068 |
| Agda | 142 | 5,483 | 3,021 |
| ANTLR | 101 | 564 | 538 |
| Apex | 253 | 17,833 | 7,561 |
| Assembly | 7,100 | 208,896 | 101,093 |
| C | 50,000 | 16,585,280 | 3,076,470 |
| C# | 50,000 | 5,906,716 | 3,257,456 |
| C++ | 50,000 | 14,891,856 | 4,469,823 |
| Clojure | 27,107 | 380,567 | 269,118 |
| Cobol | 341 | 2,242 | 1,172 |
| Common Lisp | 796 | 45,083 | 13,922 |
| Coq | 477 | 54,137 | 22,549 | 
| Cuda | 1,191 | 26,948 | 12,418 |
| Crystal | 368 | 11,606 | 6,818 |
| D | 1,185 | 185,630 | 54,034 |
| Dart | 11,907 | 484,935 | 412,675 |
| EJS | 1,475 | 15,513 | 12,832 | 
| Elixir | 2,371 | 643,856 | 102,874 |
| Emacs Lisp | 377 | 8,260 | 7,312 |
| Erlang | 1,240 | 55,932 | 27,322 |
| F# | 876 | 22,152 | 13,282 |
| Forth | 222 | 28,287 | 5,129 |
| Go | 50,000 | 8,506,379 | 2,328,529 |
| Groovy | 2,198 | 60,299 | 47,366 |
| Hack | 1,379 | 84,916 | 37,189 |
| Haskell | 8,023 | 122,788 | 106,583 |
| Java | 50,000 | 6,989,601 | 5,168,193 |
| JavaScript | 50,000 | 8,289,901 | 1,907,803 |
| Julia | 2,859 | 46,284 | 36,830 |
| Kotlin | 21,665 | 1,467,343 | 1,042,136 |
| Less | 433 | 17,276 | 7,308 |
| Lua | 42,241 | 4,605,230 | 905,120 |
| Mathematica | 1,528 | 164,498 | 21,208 |
| Matlab | 20,828 | 1,051,354 | 599,085 |
| NetLogo | 332 | 900 | 855 |
| NewLisp | 35 | 5,819 | 5,123 |
| Nix | 1,892 | 75,093 | 70,407 |
| Objective-C | 7,700 | 1,899,714 | 520,332 |
| OCaml | 1,961 | 121,890 | 60,863 |
| Pascal | 5,218 | 330,832 | 180,652 |
| Perl |14,673 | 1,798,520 | 224,753 |
| PHP | 50,000 | 12,707,727 | 3,310,243 |
| Processing | 2,950 | 24,723 | 20,304 |
| Prolog | 1,071 | 38,995 | 17,570 |
| Python | 50,000 | 2,290,182 | 1,595,919 |
| R | 44,993 | 589,139 | 11,679 |
| Raku | 158 | 1384 | 689 |
| Ruby | 13,378 | 1,579,635 | 662,915 |
| Rust | 42,847 | 2,496,177 | 802,707 |
| Scala | 5,893 | 749,370 | 210,630 |
| Scheme | 1,878 | 106,620 | 50,222 |
| Scilab | 199 | 4,531 | 3,896 |
| SQL | 130 | 47,185 | 40,800 |
| Starlark | 146 | 524 | 487 |
| Swift | 13,924 | 633,819 | 434,849 |
| Vue | 14,858 | 457,605 | 321,502 |
| WebAssembly | 68 | 834 | 544 |
| Total | 733,663 | 96,945,943 | 32,666,778 |