Adding first readme version

README.md

@@ -1,3 +1,169 @@
 ---
 license: unknown
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- readability
+- code
+- source code
+- code readability
+- java
+pretty_name: Java Code Readability Combined Dataset
+size_categories:
+- n<1K
 ---
+# 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. 
+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.
+
+
+## Dataset Details
+
+### Dataset Description
+
+- **Curated by:** Buse Raymond PL, Dorn Jonathan, Sclabrino Simone
+- **Shared by [optional]:** 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.
+
+## 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 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 combined and normalized version of existing datasets on a state-of-the-art platform.
+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 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.
+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
+
+**BibTeX:**
+Buse:
+```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}
+}
+```
+
+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}
+}
+```
+
+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},
+    booktitle={2016 IEEE 24th International Conference on Program Comprehension (ICPC)},
+    pages={1--10},
+    year={2016},
+    organization={IEEE}
+}
+```
+
+**APA:**
+- 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.
+
+## Glossary
+
+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.
+
+## Dataset Card Contact
+Feel free to contact me via [E-Mail](mailto:krodin03@ads.uni-passau.de) if you have any questions or remarks.