This repository is to support contributions for tools for the Project CodeNet dataset hosted in DAX


Project CodeNet


The goal of Project CodeNet is to provide the AI-for-Code research community with a large scale, diverse, and high quality curated dataset to drive innovation in AI techniques.

Table of Contents


A decade ago, Marc Andreessen famously wrote that "software is eating the world." Software now permeates every part of our existence; Google services combine for 2 billion lines of code, and a modern vehicle contains around 100 million lines of code. It's a monumental challenge to create, debug, maintain, and update these complex software systems. Recently, a fast-growing discipline known as AI for Code aims to help software developers improve their productivity by automating the software engineering process. AI for Code researchers have been leveraging technologies like NLP and augmenting them with code analysis and compilation techniques to perform a myriad of practical tasks, such as code search, summarization, and completion, as well as code-to-code translation. The discipline isn't limited to academic research either: Ruchir Puri, IBM Research's chief research scientist, discussed in a recent podcast how technologies from AI for Code are being used to modernize legacy software by helping migrate monolithic applications to microservices for IBM's enterprise clients.

AI for Code is poised to transition from proof-of-concept to widespread adoption. To provide a catalyst for such a tipping point, researchers at IBM Research have introduced Project CodeNet, a large-scale dataset for benchmarking and experimentation. Project CodeNet has many characteristics (large scale, diveristy, etc.) similar to ImageNet, a huge dataset for imagery that had a dramatic impact on the field of computer vision research. Project CodeNet is a large scale dataset with approximately 14 million code samples, each of which is an intended solution to one of 4000 coding problems. Project CodeNet aims to do for AI for Code what ImageNet did for computer vision.


There are a few differentiating features of Project CodeNet when compared to other similar efforts. In addition to the size of the dataset, the code samples are written in over 50 programming languages, though the dominant languages are C++, C, Python, and Java. The code samples in Project CodeNet are annotated with a rich set of information, such as the code size, memory footprint, CPU run time, and status, which indicates acceptance or error types. Over 90% of the problems come with the respective problem description, which contains a concise problem statement, specification of the input format and the output format. When available, we also extracted from the problem description sample input and output, and provide them as part of the dataset. Users can execute the accepted codes samples (over 50% of the submissions are accepted) to extract additional metadata and verify outputs from generative AI models for correctness.

Another area that Project CodeNet addressed is the quality of the data samples. From a paper by Allamanis, we learned that quite a large number of frequently used AI for Code datasets have duplicate or near-duplicate code samples, which can inflate performance metrics as much as 100%. In addition, we found that problem-submission style datasets from online judging systems can contain clusters of identical problems, which will certainly skew the performance metrics. One example is POJ-104, in which problems 26 and 62 are identical. Therefore we identified the near-duplicates and the identical problem clusters in Project CodeNet and provide these information for the benefit of the users.


In light of these issues, we have extracted several benchmark datasets from CodeNet for users to perform code classification and code similarity experiments. They have been filtered to remove identical problem clusters and near-duplicate code samples, so that performance metrics can be measured on training and test data samples with the appropriate statistics. There are two C++ benchmark datasets that are similar to the popular POJ-104 but are approximately ten times in size. We felt that the size increase is necessary, since 98% accuracy has been already achieved in code classification on POJ-104. An order of magnitude larger dataset will leave ample room to advance the state of the art with more complex neural networks and algorithms. The other two benchmark datasets are in Python and Java, which provides a different flavor because the frequent use of library functions.

Potential use cases

The rich metadata and diversity open Project CodeNet to a plethora of uses cases. The problem-submission relationship in Project CodeNet corresponds to type-4 similarity and can be used for code search and clone detection. The code samples in Project CodeNet are labeled with their acceptance status and we can explore AI techniques to distinguish correct codes from problematic ones. Project CodeNet's metadata also enables the tracking of how a submission evolves from problematic to accepted, which could be used for exploring automatic code correction. Each code sample is labeled with CPU run time and memory footprint, which can be used for regression studies and prediction. Given its wealth of programs written in a multitude of languages, Project CodeNet may serve as a valuable benchmark dataset for source-to-source translation.


To facilitate creation of customized benchmarks and dataset, we provide a set of productivity tools to aggregate codes samples based on user criteria. We are also releasing pre-processing tools to transform code samples into token sequences, simplified parse trees and other code graphs.

Models and experiments

We have performed numerous experiments on the CodeNet dataset. The goal of these experiments is to produce a set of baseline models and results for users of the CodeNet dataset to gauge their research. The run scripts and training scripts are available in the model-experiments directory. The classification and similarity experiments use the benchmark datasets we extracted from CodeNet as training and test datasets. In addition to experiments based on token sequences, we also have experiments leveraging graph neural networks (GNN). For the convenience of the users interested in GNN's, we have included the simplified parse tree (SPT) representation of the code samples for each benchmark dataset. The experiment on Masked Language Model has a companion Jupyter notebook in the notebooks directory.

Relevant links

Download the dataset

Download the full dataset in our [data repository] (

tar -zxf Project_CodeNet_full.tar.gz to uncompress and untar. The directory structure and how the code samples are organized are explained here.

The 4 benchmark datasets, Project_CodeNet_C++1000, Project_CodeNet_C++1400, Project_CodeNet_Python800, and Project_CodeNet_Java250 are included in the full dataset and are available separately in the "Archive Dataset File" column of the table in the "Get this Dataset" section in our data repository. They can be used for code classification and code similarity research as a replacement of or in addition to the dataset POJ-104.

To expedite AI for code research using graph neural networks, we have included the simplified parse tree (SPT) representation of the code samples for each benchmark dataset. They are available in the "Archive SPT File" column of the table in the "Get this Dataset" section in our data repository.

Dataset overview

The Project CodeNet Dataset consists of a very large collection of source files, extensive metadata, tooling to access the dataset and make tailored selections, and documentation.

The basis of the dataset is the data available on two online judge web sites:

  1. AIZU Online Judge
  2. AtCoder

An online judge website offers programmers an opportunity to test their skills by posing programming problems in the form of courses or contests. Users may submit their solution which is then judged by an automatic review mechanism. The outcome is reported back to the user. Both problem descriptions, user submissions and associated metadata are available for study via various REST APIs.

The first step in constructing Project CodeNet is downloading the problem descriptions and the source code submissions from the websites mentioned above, followed by reshaping and consolidating the metadata and cleaning up the inconsistencies, omissions, and mistakes in the source data itself.

Dataset statistics

The dataset comprises 13,916,868 submissions, divided into 4053 problems (of which 5 are empty). Of the submissions 53.6% (7,460,588) are accepted, 29.5% are marked as wrong answer and the remaining suffer from one of the possible rejection causes. The data contains submissions in 55 different languages, although 95% of them are coded in the six most common languages (C++, Python, Java, C, Ruby, C#). C++ is the most common language with 8,008,527 submissions (57% of the total) of which 4,353,049 are accepted. Here are 2 pie charts depicting submissions and status distribution of Project CodeNet.

Drawing Drawing

A detailed overview of the dataset statistics can be found in this spreadsheet.


The data consist of complete programs in a particular programming language. Each program is contained in a single file. The file will have a name with an extension that denotes the programming language used. (More details about the specific programming language and the version of the compiler/interpreter used, can be found in the metadata.)

Each program attempts to solve a certain programming task or problem. There are many problems and each problem might have many solutions in different languages. We refer to each program as a submission instead of a solution since it might not be complete and correct. Solutions are the accepted submissions that are compilable and executable, and at least correctly produce the expected results on all provided test cases. (Of course, according to the late Dijkstra, tests are no proof of correctness.)


The metadata provides properties of interest about the problems and their submissions. Foremost it formalizes the organization of the data and the relationship between problems, languages, and the source code files. The metadata allows for queries about the data and to make specific selections among the large collection of problems, languages, and source files.

Metadata is made available in comma-separated value (CSV) files. This allows for easy processing, even with simple command-line tools. Some of the fields in the csv files might be empty, and for submissions that are not accepted, some fields might have invalid entries such as negative numbers for CPU time. Extra checking needs to be implemented in parsing these files.

The metadata is hierarchically organized on 2 levels: the first level is the dataset level that relates to all the different problems defined by the various dataset sources. The second level is the problem level that relates to all source code submissions pertaining to a single problem or task.

Metadata and data are deliberately kept fully separated within the file system.

Metadata at the dataset level

At the dataset level there is a single CSV file (problem_list.csv) listing all the different problems. Additionally, for each problem there is a more extensive description that sets the problem and any further requirements and constraints and often provides examples of data input and expected output.

The fields and their format of this CSV file are captured by the following table:

name of column data type unit description
id string none unique anonymized id of the problem
name string none short name of the problem
dataset string none original dataset, AIZU or AtCoder
time_limit int millisecond maximum time allowed for a submission
memory_limit int KB maximum memory allowed for a submission
rating int none rating, i.e., difficulty of the problem
tags string none list of tags separated by "|"; not used
complexity string none degree of difficulty of the problem; not used

Metadata at the problem level

At the problem level there is a CSV file per problem and all content of these files is of course organized under one and the same header.

The fields and their format of this CSV file are captured by the following table:

name of column data type unit description
submission_id string none unique anonymized id of the submission
problem_id string none anonymized id of the problem
user_id string none anonymized user id of the submission
date int seconds date and time of submission in the Unix timestamp format (seconds since the epoch)
language string none mapped language of the submission (ex: C++14 -> C++)
original_language string none original language specification
filename_ext string none extension of the filename that indicates the programming language used
status string none acceptance status, or error type
cpu_time int millisecond execution time
memory int KB memory used
code_size int bytes size of the submission source code in bytes
accuracy string none number of tests passed; *Only for AIZU

Here is a table of all the possible status values. The “abbreviation” and “numeric code” are sometimes seen in the original metadata on the websites; it is listed here for reference and completeness. These fields do not occur in the Project CodeNet metadata.

status abbreviation numeric code
Compile Error CE 0
Wrong Answer WA 1
Time Limit Exceeded TLE 2
Memory Limit Exceeded MLE 3
Accepted AC 4
Judge Not Available JNA 5
Output Limit Exceeded OLE 6
Runtime Error RE 7
WA: Presentation Error PE 8
Waiting for Judging WJ
Waiting for Re-judging WR
Internal Error IE
Judge System Error

Directory structure and naming convention

The data and metadata are organized in a rigorous directory structure. At the top level sits the Project CodeNet directory with several sub-directories, data, metadata, and problem_descriptions:

  • data is further subdivided into a directory per problem and within each problem directory, directories for each language. The language directory contains all the source files supposed to be written in that particular programming or scripting language. When there are no submissions for a particular language, there will be no directory for it, but the problem directory will always be there, even if there are no submissions at all.

    The name of the directory for a programming language is the common name for the language using proper capitalization and special characters. This name is the consolidation of the names used in the metadata. Information is available about how the original language designations are mapped into the directory names and how these more general and common names are mapped to the submission file name extensions. As an example, a source could be designated c++14, which is mapped into the directory C++ (notice the capital C) and will get the extension .cpp.

  • derived holds information about near-duplicates, identical problem clusters, sample input and output for each problem, as well as the benchmarks.

  • metadata holds all the problem CSV files and the problem_list.csv file.

  • problem_descriptions holds HTML files for most problems, giving an extensive description of the problem, often accompanied with some sample input and expected output.

For the sake of creating a uniform set of metadata across all data sources, and to hide any sensitive information, some metadata fields are anonymized by randomly (but uniquely and consistently) renumbering problem, submission, and user identifiers (ids). The identifiers we use are defined by simple regular expressions:

  • problem ids are anonymized and follow this pattern: p[0-9]{5} (a p followed by exactly 5 digits).
  • submission ids are anonymized and follow this pattern: s[0-9]{9} (an s followed by exactly 9 digits).
  • user ids are anonymized and follow this pattern: u[0-9]{9} (a u followed by exactly 9 digits).

Relationships among the metadata and data

The main relationship between problem metadata and data is the fact that each metadata record (a non-header row in a problem CSV file) describes one source file and provides all information about its location. The directory structure and naming convention as stated above are implicitly assumed.

Example of getting the source file for a particular submission

Starting at a CSV metadata entry for a particular submission, here is how to get to the corresponding source file. Say that the submission id is s300682070. Either we know this is a submission to problem p00001 upfront or we can grep through all Project_CodeNet/metadata/p?????.csv files to learn that. We get a brief description of this problem by looking at the p00001 entry in the Project_CodeNet/metadata/problem_list.csv:

p00001,List of Top 3 Hills,AIZU,1000,131072,,,

We can get a more verbose description of this problem by reading Project_CodeNet/problem_descriptions/p00001.html.

The Project_CodeNet/metadata/p00001.csv file provides the info on all submissions. For our selected submission we find:


We see it is an Accepted submission in the language JavaScript with file extension .js.

The source file path therefore is: Project_CodeNet/data/p00001/JavaScript/s300682070.js

Example of getting the metadata for a particular source file

Likewise, we can play the reverse game of finding the metadata entry for a given submission source file. Say the source file is Project_CodeNet/data/p00001/JavaScript/s300682070.js.

Encoded in this file name path we see the problem id p00001 and language JavaScript and of course the submission id s300682070. We find the metadata CSV file to be: Project_CodeNet/metadata/p00001.csv. Opening that file and searching for the submission id we find the entry:


Tools to process source files

The source files of Project CodeNet represent examples of some 50+ different programming and scripting languages. Of course not all languages are equally represented: most submissions are written in the more popular languages C, C++, Java, and Python.

To complement our large dataset of source code, a suite of tools and utilities will be provided. These tools target several purposes:

  • derive statistics from the dataset
  • access the dataset files to make selections
  • preprocess the source files to extract certain information
  • facilitate conversions between popular formats


Since Project CodeNet uses the file system as storage and uses a rigorous directory structure, many (Linux) command-line utilities can be directly used to extract interesting statistics about the dataset. Utilities like ls, wc and grep are very useful. The CSV metadata can best be browsed using csvkit components like csvstat.

More elaborate statistics about the dataset can easily be retrieved using SQL queries on a database representation of the metadata. HSQLDB is a database that runs off a CSV file. Our CSV problem metadata files are simply stripped of their headers and concatenated. A suite of useful SQL queries is available. A separate document explains the necessary steps.

Access and selection

As described above, it should be easy to create specific subsets of the dataset merely by copying (or symlinking) relevant files and/or directories. For more elaborate selections based on a subset or range of problems, a subset of languages, statuses, and code sizes, several Bash scripts are available to accomplish that. These scripts reside in the tools/aggregation-scripts directory and are separately documented in this README.


We provide tools to convert code samples into a representation that can be consumed by AI algorithms

Whether and to what extent the above steps can successfully be applied to any given source file depends on several factors. Obviously, if the submission is not of Accepted status, it is to be expected that even simple tokenization will fail because of malformed lexical elements. But the situation for Accepted submissions is not always better: programmers might have used certain non-standard features of the language that happen to be accepted by a certain compiler or interpreter. Simple cases are the use of a dollar sign as part of a C identifier. For languages like C and C++ that use a pre-processor, use of macros and conditional defines can hugely change how the code ultimately looks like.


Ruchir Puri, David Kung, Geert Janssen, Giacomo Domeniconi, Vladimir Zolotov, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Ulrich Finkler.

  • Artifacts for Project CodeNet paper are not available.

    Artifacts for Project CodeNet paper are not available.

    The initiative of having an authoritative dataset is valuable. It could serve as a benchmark for different source code processing tasks like code summarization or language to language translation.

    For the CodeNet paper, results are reported for code classification and code similarity experiments. However, the models and configuration scripts for MLP, CNN, or GNN are not available, and artifacts are not made available for replication.

    I would like to know whether there is a plan to share the replication package for the CodeNet paper. I would be happy to get involved if there is any assistance required to make the replication package ready.

    opened by nashid 7
  • AttributeError: Can't pickle local object 'main.<locals>.<lambda>'

    AttributeError: Can't pickle local object 'main..'


    Trying to replicate GNN-experiment on Host OS-MacOS BigSur v11.3.1 with below python packages. Please Suggest.

    (pyg) Nitins-MacBook-Air:gnn-based-experiments nitinnanda$ python 
    ['certifi==2021.5.30', 'chardet==4.0.0', 'decorator==4.4.2', 'dill==0.3.4', 'googledrivedownloader==0.4', 'idna==2.10', 'isodate==0.6.0', 'jinja2==3.0.1', 'joblib==1.0.1', 'littleutils==0.2.2', 'markupsafe==2.0.1', 'networkx==2.5.1', 'numpy==1.20.3', 'ogb==1.3.1', 'outdated==0.2.1', 'pandas==1.2.4', 'pillow==8.2.0', 'pip==21.1.2', 'pyparsing==2.4.7', 'python-dateutil==2.8.1', 'python-louvain==0.15', 'pytz==2021.1', 'rdflib==5.0.0', 'requests==2.25.1', 'scikit-learn==0.24.2', 'scipy==1.6.3', 'setuptools==52.0.0.post20210125', 'six==1.16.0', 'threadpoolctl==2.1.0', 'torch-cluster==1.5.9', 'torch-geometric==1.7.1', 'torch-scatter==2.0.7', 'torch-sparse==0.6.10', 'torch-spline-conv==1.2.1', 'torch-summary==1.4.5', 'torch==1.9.0', 'torchsummary==1.5.1', 'torchvision==0.10.0', 'tqdm==4.61.1', 'typing-extensions==', 'urllib3==1.26.5', 'wheel==0.36.2']
    (pyg) Nitins-MacBook-Air:gnn-based-experiments nitinnanda$ 
    (pyg) Nitins-MacBook-Air:gnn-based-experiments nitinnanda$ ./ 
    PYTHONPATH: :/Users/nitinnanda/Project_CodeNet/model-experiments/gnn-based-experiments
    started experiments
    Copyright (c) 2004-2016 California Institute of Technology.
    Copyright (c) 2016-2021 The Uncertainty Quantification Foundation.
    All rights reserved.
    This software is available subject to the conditions and terms laid
    out below. By downloading and using this software you are agreeing
    to the following conditions.
    Redistribution and use in source and binary forms, with or without
    modification, are permitted provided that the following conditions
    are met::
        - Redistribution of source code must retain the above copyright
          notice, this list of conditions and the following disclaimer.
        - Redistribution in binary form must reproduce the above copyright
          notice, this list of conditions and the following disclaimer in the
          documentations and/or other materials provided with the distribution.
        - Neither the names of the copyright holders nor the names of any of
          the contributors may be used to endorse or promote products derived
          from this software without specific prior written permission.
    saved args: Namespace(batch_size=80, checkpoint='', checkpointing=1, clip=0.25, dataset='python1k', device=0, dir_data='/Users/nitinnanda/Project_CodeNet/model-experiments/gnn-based-experiments/data', dir_results='/Users/nitinnanda/Project_CodeNet/model-experiments/gnn-based-experiments/results', dir_save='/Users/nitinnanda/Project_CodeNet/model-experiments/gnn-based-experiments/saved_models', drop_ratio=0.0, emb_dim=300, epochs=1000, feat_nums='', filename='gcn_lr1e-3', gnn='gcn', lr=0.001, num_layer=5, num_workers=80, patience=20.0, runs=10)
    Data loading done!
    /usr/local/Caskroom/miniconda/base/envs/pyg/lib/python3.8/site-packages/torch/utils/data/ UserWarning: This DataLoader will create 80 worker processes in total. Our suggested max number of worker in current system is 4 (`cpuset` is not taken into account), which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
    Let's use 0 GPUs! -- DataParallel running also on CPU only
    =====Run 1, Epoch 1
    Iteration:   0%|                                                                                                                        | 0/8 [00:00<?, ?it/s]
    Traceback (most recent call last):
      File "", line 321, in <module>
      File "", line 246, in main
        loss, train_perf = train(model, device, train_loader, optimizer, args, evaluator)
      File "", line 35, in train
        for step, batch in enumerate(tqdm(loader, desc="Iteration")):
      File "/usr/local/Caskroom/miniconda/base/envs/pyg/lib/python3.8/site-packages/tqdm/", line 1178, in __iter__
        for obj in iterable:
      File "/usr/local/Caskroom/miniconda/base/envs/pyg/lib/python3.8/site-packages/torch/utils/data/", line 359, in __iter__
        return self._get_iterator()
      File "/usr/local/Caskroom/miniconda/base/envs/pyg/lib/python3.8/site-packages/torch/utils/data/", line 305, in _get_iterator
        return _MultiProcessingDataLoaderIter(self)
      File "/usr/local/Caskroom/miniconda/base/envs/pyg/lib/python3.8/site-packages/torch/utils/data/", line 918, in __init__
      File "/usr/local/Caskroom/miniconda/base/envs/pyg/lib/python3.8/multiprocessing/", line 121, in start
        self._popen = self._Popen(self)
      File "/usr/local/Caskroom/miniconda/base/envs/pyg/lib/python3.8/multiprocessing/", line 224, in _Popen
        return _default_context.get_context().Process._Popen(process_obj)
      File "/usr/local/Caskroom/miniconda/base/envs/pyg/lib/python3.8/multiprocessing/", line 284, in _Popen
        return Popen(process_obj)
      File "/usr/local/Caskroom/miniconda/base/envs/pyg/lib/python3.8/multiprocessing/", line 32, in __init__
      File "/usr/local/Caskroom/miniconda/base/envs/pyg/lib/python3.8/multiprocessing/", line 19, in __init__
      File "/usr/local/Caskroom/miniconda/base/envs/pyg/lib/python3.8/multiprocessing/", line 47, in _launch
        reduction.dump(process_obj, fp)
      File "/usr/local/Caskroom/miniconda/base/envs/pyg/lib/python3.8/multiprocessing/", line 60, in dump
        ForkingPickler(file, protocol).dump(obj)
    AttributeError: Can't pickle local object 'main.<locals>.<lambda>'
    (pyg) Nitins-MacBook-Air:gnn-based-experiments nitinnanda$ 
    opened by singlelionwith2cubs 7
  • Metadata information not found (problem_list.csv)

    Metadata information not found (problem_list.csv)


    I am in search of description file, which can describe a brief summary depicting the implementation details of each set of problem in Project_CodeNet_Java250 dataset.

    Right now, dataset is available without any description of meta data. I tried to find a file named as "problem_list.csv", but in vain. Can you please share this information?

    opened by states786 6
  • Cannot get train, valid and test set from the corpus.

    Cannot get train, valid and test set from the corpus.


    Thanks for this wonderful project. One question is that when I look through the data, I found that it has not split into train/valid/test. Can you help to split the data accordingly, so we can compare it with the numbers posted in the table.


    Thanks and best regards.

    opened by shangqing-liu 5
  • Lack of documentation on SPT(json) split into CSVs to be fed to trained GNN model for code classification experiment

    Lack of documentation on SPT(json) split into CSVs to be fed to trained GNN model for code classification experiment


    Created SPT from for given sample C code.

    Nitins-MacBook-Air:spt-generator nitinnanda$ ./scripts/run/ -d=/Users/nitinnanda/Downloads/SPT ./examples/c/helloworld.c
    /Users/nitinnanda/Downloads/SPT/helloworld.json is generated!
    /Users/nitinnanda/Downloads/SPT/helloworld.csv is generated.
    Nitins-MacBook-Air:spt-generator nitinnanda$ 

    Created trained model from

    (pyg) Nitins-MacBook-Air:saved_models nitinnanda$ ls -ltr
    total 11400
    -rw-r--r--  1 nitinnanda  staff  5834983 Jun 21 18:07
    (pyg) Nitins-MacBook-Air:saved_models nitinnanda$ 

    Now the SPT here helloworld.json needs to be split into CSVs to feed to trained model however, there is very little to no documentation given on this split.

    Please guide.

    opened by singlelionwith2cubs 5
  • Backdoor detected

    Backdoor detected

    Apologies if this has already been reported, but Windows Security is detecting a backdoor threat Backdoor.PHP/Dirtelti.MTF in 4 php files in the download from this repository

    BackdoorPHP/Dirtelti.MTF Alert level: Severe Status: Active Date: 2021-06-05 10:58 Category: Backdoor Details: This program provides remote access to the computer it is installed on. Learn more Affected items: containerfile: C:\Users\b\DownIoads\Project_CodeNettar.gz containerfile: C:\Windows\Temp\TMPDB22CA7DF94B81B2 file: C:\Users\b\DownIoads\Project_CodeNet.tar.gz->(GZip)->Project_CodeNet/data/p03844/PHP/s069566612.php file: C:\Users\b\DownIoads\Project_CodeNet.tar.gz->(GZip)->Project_CodeNet/data/p03844/PHP/s308064656.php file: C:\Users\b\DownIoads\Project_CodeNet.tar.gz->(GZip)->Project_CodeNet/data/p03844/PHP/s6862216O0.php file: C:\Users\b\DownIoads\Project_CodeNet.tar.gz->(GZip)->Project_CodeNet/data/p03844/PHP/s967662473.php

    opened by benreaves 5
  • Does the metadata include compiler version and execution platform information

    Does the metadata include compiler version and execution platform information


    Thank you for sharing this wonderful projects. You've annotated a rich set of information in metadata, but does the metadata include compiler version and execution platform information.

    I think the compiler version or execution platform(ex.x86 ) may impact the execute result, and some programming syntax.

    So how can we know the compiler version and platform information for the code sample in the dataset? If these information were not included now, is it possible to add them?

    Thank you!

    opened by doomsdayciel 4
  • Why do you choose AIZU and AtCoder's problem and codesample as raw CodeNet's rawdata?

    Why do you choose AIZU and AtCoder's problem and codesample as raw CodeNet's rawdata?


    Thank you for sharing this project. I know that the codesample and problem_description is from AIZU & AtCoder The basis of the dataset is the data available on two online judge web sites:

    AIZU Online Judge AtCoder

    I want to ask why do you choose AIZU & AtCoder? As there are a lot of online judge web sites, Is there anything special about these two sites , and will you add code sample and problem_descritption from other online judge web sites or other web sites in the future?

    Looking forward to your reply, thank you!

    opened by doomsdayciel 4
  • Would Loyc trees be useful for ML-based language translation and prediction?

    Would Loyc trees be useful for ML-based language translation and prediction?

    I'm not a machine learning expert by any means. However, I think that there may be some value in running multi-language ML models against an intermediate language rather than against the original source text. For example, the following two statements are essentially identical in meaning, but not in form:

    if (x > 0 && x <= y) {
      process(x, y);
    } else {
      addError("Out of range");
    if x > 0 and x <= y:
      process(x, y);
      addError("Out of range");

    For some years I've been developing an interchange format for code, which would map these two blocks to an identical intermediate form. While ML models can presumably learn all by themselves that these two blocks are "the same", I suspect it would be beneficial to "help" models understand common similarities in a more explicit way.

    By analogy, 7-zip can compress a file down to a lower size when the original file is already expressed more efficiently (e.g. given an xml file and a binary file representing the same data, the size of the compressed binary file is smaller, even if the compression ratio isn't as good). Similarly, I speculate that adjusting different languages to be more similar to each other in a preprocessing step would spare the model from having to deal with irrelevant syntactic differences, allowing it to dedicate more resources to the (still very numerous) remaining differences.

    Since I only work on this in my free time (and I have several other free-time projects), important parts of a Loyc tree standard are un/underdeveloped. Even so I would like to hear from ML researchers about whether something like this could be a useful tool for them.

    opened by qwertie 4
  • Feature extraction for GNN example

    Feature extraction for GNN example


    Thanks for release the dataset with baseline methods. I have a question regarding the GNN example in . The data is preprocessed into the form of .csv files. May I know how I can obtain these files from the original c++1000 dataset? I'm asking partly because there seems to some additional treatment such as the next node token on top of the SPT representation, so it would be great to have a step by step tutorial of obtaining the processed data in the first place.

    I'm working on a project using the dataset, so your timely reply will be invaluable. Thanks in advance.

    opened by EricYizhenWang 4
  • Script for testing Input-Output

    Script for testing Input-Output

    In the README file for the input-output data in CodeNet the authors say - "For every problem, input was fed into accepted solution programs in order to check if the output from the solution program matched the output file". Would it be possible for the authors to share the script they used to automate the above process?

    Also, another question I had was regarding the "verified" tag given to problems. The README file associated with the input-output data says -

    "We considered problem input and output to be "verified" if program output matched the output file for at least one tested program in any of the four programming languages used. As mentioned, there may be differences in formatting of solutions. Thus, "verified" problem input will not always produce output that is identical to the output file in accepted programs."

    My question here is, what then is the purpose of the "verified" tag if it does not ensure that the produced output matches the specifications given in the output file?

    Any help regarding these issues will be greatly appreciated. Thanks in advance!

    opened by karths8 1
  • Fix the link markdown in

    Fix the link markdown in

    "Download the full dataset in our [data repository] ("

    The markdown syntax used here is incorrect.

    opened by geert56 0
  • Geert's updates to tokenizer; new tokml program.

    Geert's updates to tokenizer; new tokml program.

    The C/C++ and Java tokenizer has been revamped. A library has been split off so that it is easy to use the tokenizer from Python code using "ctypes". There is a new program called tokml. All documentation in has been updated. Only the dir tools/tokenizer is affected.

    opened by geert56 0
International Business Machines
International Business Machines
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