| | --- |
| | language: |
| | - en |
| | license: unknown |
| | size_categories: |
| | - n<1K |
| | task_categories: |
| | - text-classification |
| | pretty_name: Java Code Readability Merged Dataset |
| | tags: |
| | - readability |
| | - code |
| | - source code |
| | - code readability |
| | - Java |
| | features: |
| | - name: code_snippet |
| | dtype: string |
| | - name: score |
| | dtype: float |
| | dataset_info: |
| | features: |
| | - name: code_snippet |
| | dtype: string |
| | - name: score |
| | dtype: float64 |
| | splits: |
| | - name: train |
| | num_bytes: 354539 |
| | num_examples: 421 |
| | download_size: 139793 |
| | dataset_size: 354539 |
| | configs: |
| | - config_name: default |
| | data_files: |
| | - split: train |
| | path: data/train-* |
| | --- |
| | |
| | # Java Code Readability Merged Dataset |
| |
|
| | This dataset contains **421 Java code snippets** along with a **readability score**, aggregated from several scientific papers [1, 2, 3]. |
| |
|
| | You can download the dataset using Hugging Face: |
| |
|
| | ```python |
| | from datasets import load_dataset |
| | ds = load_dataset("se2p/code-readability-merged") |
| | ``` |
| |
|
| | The snippets are **not** split into train and test (and validation) set. Thus, the whole dataset is in the **train** set: |
| | ```python |
| | ds = ds['train'] |
| | ds_as_list = ds.to_list() # Convert the dataset to whatever format suits you best |
| | |
| | ``` |
| |
|
| | The dataset is structured as follows: |
| |
|
| | ```json |
| | { |
| | "code_snippet": ..., # Java source code snippet |
| | "score": ... # Readability score |
| | } |
| | ``` |
| |
|
| | The main goal of this repository is to train code **readability classifiers for Java source code**. |
| | The dataset is a combination and normalization of three datasets: |
| |
|
| | 1. **Buse**, R. P., & Weimer, W. R. (2009). Learning a metric for code readability. IEEE Transactions on software engineering, 36(4), 546-558. |
| | 2. **Dorn**, J. (2012). A General Software Readability Model. |
| | 3. **Scalabrino**, S., Linares‐Vásquez, M., Oliveto, R., & Poshyvanyk, D. (2018). A comprehensive model for code readability. Journal of Software: Evolution and Process, 30(6), e1958. |
| |
|
| | The raw datasets can be downloaded [here](https://dibt.unimol.it/report/readability/). |
| |
|
| | ## Dataset Details |
| |
|
| | ### Dataset Description |
| |
|
| | - **Curated by:** Buse Raymond PL, Dorn Jonathan, Sclabrino Simone |
| | - **Shared by:** Krodinger Lukas |
| | - **Language(s) (NLP):** Java |
| | - **License:** Unknown |
| |
|
| | ## Uses |
| |
|
| | The dataset can be used for training Java code readability classifiers. |
| |
|
| | ## Dataset Structure |
| |
|
| | Each entry of the dataset consists of a **code_snippet** and a **score**. |
| | The code_snippet (string) is the code snippet that was rated in a study by multiple participants. |
| | Those could answer based on a five point Likert scale, with 1 being very unreadable and 5 being very readable. |
| | The score (float) is the averaged rating score of all participants between 1.0 (very unreadable) and 5.0 (very readable). |
| | |
| | ## Dataset Creation |
| | |
| | ### Curation Rationale |
| | |
| | To advance code readability classification, the creation of datasets in this research field is of high importance. |
| | As a first step, we provide a merged and normalized version of existing datasets on Hugging Face. |
| | This makes access and ease of usage of this existing data easier. |
| | |
| | ### Source Data |
| | |
| | The source of the data are the papers from Buse, Dorn and Scalabrino. |
| | |
| | Buse conducted a survey with 120 computer science students (17 from first year courses, 63 from second year courses, 30 third or fourth year courses, 10 graduated) on 100 code snippets. |
| | The code snippets were generated from five open source Java projects. |
| | |
| | Dorn conducted a survey with 5000 participants (1800 with industry experience) on 360 code snippets from which 121 are Java code snippets. |
| | The used snippets were drawn from ten open source projects in the SourceForge repository (of March 15, 2012). |
| | |
| | Scalabrino conducted a survey with 9 computer science students on 200 new code snippets. |
| | The snippets were selected from four open source Java projects: jUnit, Hibernate, jFreeChart and ArgoUML. |
| | |
| | |
| | #### Data Collection and Processing |
| | |
| | The dataset was preprocessed by **averaging the readability rating** for each code snippet. |
| | The code snippets and ratings were then **merged** from the three sources. |
| |
|
| | Each of the three, Buse, Dorn and Sclabrino selected their code snippets based on different criteria. |
| | They had a different number of participants for their surveys. |
| | One could argue that a code snippet that was rated by more participants might have a more accurate readability score and therefore is more valuable than one with less ratings. |
| | However, for simplicity those differences are ignored. |
| |
|
| | Other than the selection (and generation) done by the original data source authors, no further processing is applied to the data. |
| |
|
| | #### Who are the source data producers? |
| |
|
| | The source data producers are the people that wrote the used open source Java projects, as well as the study participants, which were mostly computer science students. |
| |
|
| | #### Personal and Sensitive Information |
| |
|
| | The ratings of the code snippets are anonymized and averaged. Thus, no personal or sensitive information is contained in this dataset. |
| |
|
| | ## Bias, Risks, and Limitations |
| |
|
| | The size of the dataset is very **small**. |
| | The ratings of code snippets were done mostly by **computer science students**, who do not represent the group of Java programmers in general. |
| |
|
| | ### Recommendations |
| |
|
| | The dataset should be used to train **small** Java code readability classifiers. |
| |
|
| | ## Citation |
| |
|
| | 1. Buse, R. P., & Weimer, W. R. (2009). Learning a metric for code readability. IEEE Transactions on software engineering, 36(4), 546-558. |
| | 2. Dorn, J. (2012). A General Software Readability Model. |
| | 3. Scalabrino, S., Linares‐Vásquez, M., Oliveto, R., & Poshyvanyk, D. (2018). A comprehensive model for code readability. Journal of Software: Evolution and Process, 30(6), e1958. |
| |
|
| | ```bibtex |
| | @article{buse2009learning, |
| | title={Learning a metric for code readability}, |
| | author={Buse, Raymond PL and Weimer, Westley R}, |
| | journal={IEEE Transactions on software engineering}, |
| | volume={36}, |
| | number={4}, |
| | pages={546--558}, |
| | year={2009}, |
| | publisher={IEEE} |
| | } |
| | |
| | @inproceedings{dorn2012general, |
| | title={A General Software Readability Model}, |
| | author={Jonathan Dorn}, |
| | year={2012}, |
| | url={https://api.semanticscholar.org/CorpusID:14098740} |
| | } |
| | |
| | @article{scalabrino2018comprehensive, |
| | title={A comprehensive model for code readability}, |
| | author={Scalabrino, Simone and Linares-V{\'a}squez, Mario and Oliveto, Rocco and Poshyvanyk, Denys}, |
| | journal={Journal of Software: Evolution and Process}, |
| | volume={30}, |
| | number={6}, |
| | pages={e1958}, |
| | year={2018}, |
| | publisher={Wiley Online Library} |
| | } |
| | ``` |
| |
|
| | ## Dataset Card Authors |
| | Lukas Krodinger, [Chair of Software Engineering II](https://www.fim.uni-passau.de/en/chair-for-software-engineering-ii), [University of Passau](https://www.uni-passau.de/en/). |
| |
|
| | ## Dataset Card Contact |
| | Feel free to contact me via [E-Mail](mailto:krodin03@ads.uni-passau.de) if you have any questions or remarks. |
