SANER 2024 Paper

Our paper ‘T-FREX: A Transformer-based Feature Extraction Method from Mobile App Reviews’ (with Quim Motger, Felice Dell’Orletta, Xavier Franch and Jordi Marco) has been accepted at the IEEE International Conference on Software Analysis, Evaluation and Reengineering (SANER) 2024. In the paper we present T-FREX, a Transformer-based, fully automatic approach for mobile app review feature extraction. First, we collect a set of ground truth features from users in a real crowdsourced software recommendation platform and transfer them automatically into a dataset of app reviews. Then, we use this newly created dataset to fine-tune multiple LLMs on a named entity recognition task under different data configurations. We assess the performance of T-FREX with respect to this ground truth, and we complement our analysis by comparing T-FREX with a baseline method from the field. Finally, we assess the quality of new features predicted by T-FREX through an external human evaluation. Results show that T-FREX outperforms on average the traditional syntactic-based method, especially when discovering new features from a domain for which the model has been fine-tuned.