---
dataset_info:
- config_name: dublin_data
  features:
  - name: submission_id
    dtype: int32
  - name: func_code
    dtype: string
  - name: assignment_id
    dtype: string
  - name: func_name
    dtype: string
  - name: description
    dtype: string
  - name: test
    dtype: string
  - name: user
    dtype: string
  - name: academic_year
    dtype: int32
  splits:
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    num_bytes: 3659009
    num_examples: 6117
  - name: test
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    num_examples: 9885
  download_size: 12774289
  dataset_size: 10101225
- config_name: singapore_data
  features:
  - name: submission_id
    dtype: int32
  - name: func_code
    dtype: string
  - name: assignment_id
    dtype: string
  - name: func_name
    dtype: string
  - name: description
    dtype: string
  - name: test
    dtype: string
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- config_name: dublin_repair
  features:
  - name: submission_id
    dtype: int32
  - name: func_code
    dtype: string
  - name: assignment_id
    dtype: string
  - name: func_name
    dtype: string
  - name: description
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  - name: test
    dtype: string
  - name: annotation
    dtype: string
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  - name: test
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- config_name: singapore_repair
  features:
  - name: submission_id
    dtype: int32
  - name: func_code
    dtype: string
  - name: assignment_id
    dtype: string
  - name: func_name
    dtype: string
  - name: description
    dtype: string
  - name: test
    dtype: string
  - name: annotation
    dtype: string
  splits:
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- config_name: dublin_metadata
  features:
  - name: assignment_id
    dtype: string
  - name: func_name
    dtype: string
  - name: reference_solution
    dtype: string
  - name: description
    dtype: string
  - name: test
    dtype: string
  splits:
  - name: train
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    num_examples: 30
  - name: test
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  download_size: 37317
  dataset_size: 32904
- config_name: singapore_metadata
  features:
  - name: assignment_id
    dtype: string
  - name: func_name
    dtype: string
  - name: reference_solution
    dtype: string
  - name: description
    dtype: string
  - name: test
    dtype: string
  splits:
  - name: train
    num_bytes: 5577
    num_examples: 5
  download_size: 6139
  dataset_size: 5577
license: cc
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: inpro
size_categories:
- 10K<n<100K
---

# Dataset Card for intro_prog

## Dataset Description

### Dataset Summary

IntroProg is a collection of students' submissions to assignments in various introductory programming courses offered at different universities.
Currently, the dataset contains submissions collected from Dublin City University, and the University of Singapore.

#### Dublin

The Dublin programming dataset is a dataset composed of students' submissions to introductory programming assignments at the University of Dublin. 
Students submitted these programs for multiple programming courses over the duration of three academic years.

#### Singapore 

The Singapore dataset contains 2442 correct and 1783 buggy program attempts by 361 undergraduate students 
crediting an introduction to Python programming course at NUS (National University of Singapore).


### Supported Tasks and Leaderboards

#### "Metadata": Program synthesis

Similarly to the [Most Basic Python Programs](https://huggingface.co/datasets/mbpp) (mbpp), the data split can be used to evaluate
code generations models.

#### "Data"

The data configuration contains all the submissions as well as an indicator of whether these passed the required test. 
 
#### "repair": Program refinement/repair

The "repair" configuration of each dataset is a subset of the "data" configuration
augmented with educators' annotations on the corrections to the buggy programs. 
This configuration can be used for the task of program refinement. In [Computing Education Research](https://faculty.washington.edu/ajko/cer/) (CER), 
methods for automatically repairing student programs are used to provide students with feedback and help them debug their code.

#### "bug": Bug classification

[Coming soon]

### Languages

The assignments were written in Python. 

## Dataset Structure

One configuration is defined by one source dataset *dublin* or *singapore* and one subconfiguration ("metadata", "data", or "repair"):

* "dublin_metadata"
* "dublin_data"
* "dublin_repair"
* "singapore_metadata"
* "singapore_data"
* "singapore_repair"


### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

Some of the fields are configuration specific

* submission_id: a unique number identifying the submission
* user: a unique string identifying the (anonymized) student who submitted the solution
* date: the timestamp at which the grading server received the submission
* func_code: the cleaned code submitted
* func_name: the name of the function that had to be implemented
* assingment_id: the unique (string) identifier of the assignment that had to be completed
* academic_year: the starting year of the academic year (e.g. 2015 for the academic year 2015-2016)
* module: the course/module
* test: a human eval-style string which can be used to execute the submitted solution on the provided test cases
* Description: a description of what the function is supposed to achieve
* correct: whether the solution passed all tests or not


### Data Splits

#### Dublin

The Dublin dataset is split into a training and validation set. The training set contains the submissions to the assignments
written during the academic years 2015-2016, and 2016-2017, while the test set contains programs written during the academic year 2017-2018. 

#### Singapore

The Singapore dataset only contains a training split, which can be used as a test split for evaluating how your feedback
methods perform on an unseen dataset (if, for instance, you train your methods on the Dublin Dataset). 

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information


#### Dublin 

#### Singapore

The data was released under a [GNU Lesser General Public License v3.0](https://github.com/githubhuyang/refactory/blob/master/LICENSE) license


### Citation Information

```
@inproceedings{azcona2019user2code2vec,
  title={user2code2vec: Embeddings for Profiling Students Based on Distributional Representations of Source Code},
  author={Azcona, David and Arora, Piyush and Hsiao, I-Han and Smeaton, Alan},
  booktitle={Proceedings of the 9th International Learning Analytics & Knowledge Conference (LAK’19)},
  year={2019},
  organization={ACM}
}
@inproceedings{DBLP:conf/edm/CleuziouF21,
  author    = {Guillaume Cleuziou and
               Fr{\'{e}}d{\'{e}}ric Flouvat},
  editor    = {Sharon I{-}Han Hsiao and
               Shaghayegh (Sherry) Sahebi and
               Fran{\c{c}}ois Bouchet and
               Jill{-}J{\^{e}}nn Vie},
  title     = {Learning student program embeddings using abstract execution traces},
  booktitle = {Proceedings of the 14th International Conference on Educational Data
               Mining, {EDM} 2021, virtual, June 29 - July 2, 2021},
  publisher = {International Educational Data Mining Society},
  year      = {2021},
  timestamp = {Wed, 09 Mar 2022 16:47:22 +0100},
  biburl    = {https://dblp.org/rec/conf/edm/CleuziouF21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```

### Contributions

[More Information Needed]