Task Categories: text-scoring
Languages: en
Multilinguality: monolingual
Size Categories: 100K<n<1M
Licenses: unknown
Language Creators: crowdsourced
Annotations Creators: crowdsourced
Source Datasets: original

Dataset Card for [Dataset Name]

Dataset Summary

It is a large dataset of Android applications belonging to 23 differentapps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versions, 280,000 user reviews (extracted with specific text mining approaches)

Supported Tasks and Leaderboards

The dataset we provide comprises 395 different apps from F-Droid repository, including code quality indicators of 629 versions of these apps. It also encloses app reviews related to each of these versions, which have been automatically categorized classifying types of user feedback from a software maintenance and evolution perspective.


The dataset is a monolingual dataset which has the messages English.

Dataset Structure

Data Instances

The dataset consists of a message in English.

{'package_name': 'com.mantz_it.rfanalyzer', 'review': "Great app! The new version now works on my Bravia Android TV which is great as it's right by my rooftop aerial cable. The scan feature would be useful...any ETA on when this will be available? Also the option to import a list of bookmarks e.g. from a simple properties file would be useful.", 'date': 'October 12 2016', 'star': 4}

Data Fields

  • package_name : Name of the Software Application Package
  • review : Message of the user
  • date : date when the user posted the review
  • star : rating provied by the user for the application

Data Splits

There is training data, with a total of : 288065

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]


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

With the help of this dataset one can try to understand more about software applications and what are the views and opinions of the users about them. This helps to understand more about which type of software applications are prefeered by the users and how do these applications facilitate the user to help them solve their problems and issues.

Discussion of Biases

The reviews are only for applications which are in the open-source software applications, the other sectors have not been considered here

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

Giovanni Grano - (University of Zurich), Sebastiano Panichella - (University of Zurich), Andrea di Sorbo - (University of Sannio)

Licensing Information

[More Information Needed]

Citation Information

@InProceedings{Zurich Open Repository and Archive:dataset, title = {Software Applications User Reviews}, authors={Grano, Giovanni; Di Sorbo, Andrea; Mercaldo, Francesco; Visaggio, Corrado A; Canfora, Gerardo; Panichella, Sebastiano}, year={2017} }


Thanks to @darshan-gandhi for adding this dataset.

Models trained or fine-tuned on app_reviews

None yet