Working on readme

README.md

@@ -9,63 +9,74 @@ tags:
 - code
 - source code
 - code readability
-- java
+- Java
 pretty_name: Java Code Readability Combined Dataset
 size_categories:
 - n<1K
+features:
+  - name: code_snippet
+    dtype: string
+  - name: score
+    dtype: float
 ---
+
 # Java Code Readability Combined Dataset
 
-This dataset contains 421 java code snippets along with a readability score. The snippets are not split into train & test (& validation) set yet.
-The main goal of this repository is to train code readability classifiers for java source code. 
+This dataset contains **421 Java code snippets** along with a **readability score**. 
+
+You can download the dataset using Hugging Face:
+
+```python
+from datasets import load_dataset
+ds = load_dataset("LuKrO/code-readbility-combined")
+```
+
+The dataset is structured as follows:
+
+```python
+{
+  "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: 
 
 - **Buse**, Raymond PL, and Westley R. Weimer. "Learning a metric for code readability." IEEE Transactions on software engineering 36.4 (2009): 546-558.
 - **Dorn**, Jonathan. “A General Software Readability Model.” (2012).
 - **Scalabrino**, Simone, et al. "Automatically assessing code understandability: How far are we?." 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 2017.
 
-The raw datasets can be downloaded [here](https://dibt.unimol.it/report/readability/). 
-The datasets are generated by asking java programmers how readable they rate the given snippet. 
-Participants could then answer based on a five point Likert scale, with 1 being very unreadable and 5 being very readable.
-
-We normalized the raw survey result by averaging for each java code snippet the readability rating over all participants.
-This results in a readability rating between 1.0 to 5.0 per snippet.
-The snippets with the average rating of the three authors were then combined into  the given dataset.
-
+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 [optional]:** Krodinger Lukas
+- **Shared by:** Krodinger Lukas
 - **Language(s) (NLP):** Java
 - **License:** Unknown
 
-### Dataset Sources [optional]
-
-- **Origin:** https://dibt.unimol.it/report/readability/
-- **Paper:**
-  - **Buse**, Raymond PL, and Westley R. Weimer. "Learning a metric for code readability." IEEE Transactions on software engineering 36.4 (2009): 546-558.
-  - **Dorn**, Jonathan. “A General Software Readability Model.” (2012).
-  - **Scalabrino**, Simone, et al. "Improving code readability models with textual features." 2016 IEEE 24th International Conference on Program Comprehension (ICPC). IEEE, 2016.
-
 ## Uses
 
-The dataset can be used for training java code readability classifiers.
+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. 
+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).
 
+The snippets are **not** split into train and test (and validation) set.
+
 ## 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 combined and normalized version of existing datasets on a state-of-the-art platform.
+As a first step, we provide a combined and normalized version of existing datasets on Hugging Face.
 This makes access and ease of usage of this existing data easier.
 
 ### Source Data
@@ -73,24 +84,23 @@ This makes access and ease of usage of this existing data easier.
 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.
+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.
+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 combined from the three sources.
+The dataset was preprocessed by **averaging the readability rating** for each code snippet. 
+The code snippets and ratings were then **combined** 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.
-Note that those differences were ignored when combining the datasets.
-For example, 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.
+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.
@@ -105,8 +115,8 @@ The ratings of the code snippets are anonymized and averaged. Thus, no personal
 
 ## 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.
+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
 
@@ -115,7 +125,6 @@ The dataset should be used to train **small** Java code readability classifiers.
 ## Citation
 
 **BibTeX:**
-Buse:
 ```bibtex
 @article{buse2009learning,
     title={Learning a metric for code readability},
@@ -127,22 +136,14 @@ Buse:
     year={2009},
     publisher={IEEE}
 }
-```
 
-Dorn:
-```bibtex
-@article{dorn2012general,
-    title={A general software readability model},
-    author={Dorn, Jonathan},
-    journal={MCS Thesis available from (http://www. cs. virginia. edu/weimer/students/dorn-mcs-paper. pdf)},
-    volume={5},
-    pages={11--14},
-    year={2012}
+@inproceedings{dorn2012general,
+  title={A General Software Readability Model},
+  author={Jonathan Dorn},
+  year={2012},
+  url={https://api.semanticscholar.org/CorpusID:14098740}
 }
-```
 
-Scalabrino:
-```bibtex
 @inproceedings{scalabrino2016improving,
     title={Improving code readability models with textual features},
     author={Scalabrino, Simone and Linares-Vasquez, Mario and Poshyvanyk, Denys and Oliveto, Rocco},
@@ -163,7 +164,7 @@ Scalabrino:
 Readability: We define readability as a subjective impression of the difficulty of code while trying to understand it.
 
 ## 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.
+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.