ARdoc: app reviews development oriented classifier

@inproceedings{Panichella2016ARdocAR,
  title={ARdoc: app reviews development oriented classifier},
  author={Sebastiano Panichella and Andrea Di Sorbo and Emitza Guzman and Corrado Aaron Visaggio and Gerardo Canfora and Harald C. Gall},
  booktitle={SIGSOFT FSE},
  year={2016}
}
Google Play, Apple App Store and Windows Phone Store are well known distribution platforms where users can download mobile apps, rate them and write review comments about the apps they are using. Previous research studies demonstrated that these reviews contain important information to help developers improve their apps. However, analyzing reviews is challenging due to the large amount of reviews posted every day, the unstructured nature of reviews and its varying quality. In this demo we… CONTINUE READING
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Key Quantitative Results

  • Our quantitative and qualitative analysis (involving mobile professional developers) demonstrates that ARdoc correctly classifies feedback useful for maintenance perspectives in user reviews with high precision (ranging between 84% and 89%), recall (ranging between 84% and 89%), and F-Measure (ranging between 84% and 89%).

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