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Android apps and user feedback: a dataset for software evolution and quality improvement
| Content Provider | ACM Digital Library |
|---|---|
| Author | Di Sorbo, Andrea Canfora, Gerardo Grano, Giovanni Mercaldo, Francesco Panichella, Sebastiano Visaggio, Corrado A. |
| Abstract | Nowadays, Android represents the most popular mobile platform with a market share of around 80%. Previous research showed that data contained in user reviews and code change history of mobile apps represent a rich source of information for reducing software maintenance and development effort, increasing customers' satisfaction. Stemming from this observation, we present in this paper a large dataset of Android applications belonging to 23 different apps 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 and more than 450,000 user feedback (extracted with specific text mining approaches). Furthermore, for each app version in our dataset, we employed the Paprika tool and developed several Python scripts to detect 8 different code smells and compute 22 code quality indicators. The paper discusses the potential usefulness of the dataset for future research in the field. . |
| Starting Page | 8 |
| Ending Page | 11 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450351584 |
| DOI | 10.1145/3121264.3121266 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-09-05 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | App reviews Software maintenance and evolution Software quality Mobile applications |
| Content Type | Text |
| Resource Type | Article |
