Automatic Classifying of Requirements-relevant contents from App Reviews in the Arabic Language

The market for mobile application development continues to thrive with billions of users and millions of apps. Collecting software requirements for mobile apps has to cope with this trend, so as for the software to compete in this crowded scene. Therefore, efforts to analyze mobile app reviews for requirements have shown a similar trend of increase. Among the billions of mobile users, there are hundreds of millions of Arabic-speaking users. According to our knowledge, this study would be one of the first studies in the field of mining mobile app reviews for the assistance of requirements engineering, to direct its focus on Arabic reviews. The main contribution of this study is to provide a framework for mining mobile app reviews in Arabic. A dataset of 7604 Arabic app reviews has been constructed and manually annotated by six experts. Each categorization aims at assisting one or more processes of software requirements engineering. Three configurations of deep neural networks, namely, CNN, LSTM, and BLSTM, were used to classify the app reviews into the considered categories of software requirements from the Arabic reviews. Furthermore, two word embeddings were utilized, on pre-trained models; Fasttext and Word2Vec, produced by this study. The sentimental analysis results show that the LSTM classifier with the Fasttext word embeddings gives the best F1-score, 79.17%. However, the BLSTM classifier with the fastText embeddings outperforms the other classifiers, with an F1-score of 69.83%, when used for identifying the sub-categories of the user perspective main category. The F1-score for classifying the sub-categories of the intention and topics with the LSTM and using fastText embeddings is 82.68% and 85,02%, respectively. These results outperform the other configurations of the classifiers and word embeddings. These results demonstrate the potential of our system to serve as a robust tool for automating software requirement extraction from Arabic app reviews, particularly in contexts where real-time user feedback is critical to agile development cycles.