Automatic Classification of Non-Functional Requirements from Augmented App User Reviews

Abstract

Context: The leading App distribution platforms, Apple App Store, Google Play, and Windows Phone Store, have over 4 million Apps. Research shows that user reviews contain abundant useful information which may help developers to improve their Apps. Extracting and considering Non-Functional Requirements (NFRs), which describe a set of quality attributes wanted for an App and are hidden in user reviews, can help developers to deliver a product which meets users' expectations. Objective: Developers need to be aware of the NFRs from massive user reviews during software maintenance and evolution. Automatic user reviews classification based on an NFR standard provides a feasible way to achieve this goal. Method: In this paper, user reviews were automatically classified into four types of NFRs (reliability, usability, portability, and performance), Functional Requirements (FRs), and Others. We combined four classification techniques BoW, TF-IDF, CHI2, and AUR-BoW (proposed in this work) with three machine learning algorithms Naive Bayes, J48, and Bagging to classify user reviews. We conducted experiments to compare the F-measures of the classification results through all the combinations of the techniques and algorithms. Results: We found that the combination of AUR-BoW with Bagging achieves the best result (a precision of 71.4%, a recall of 72.3%, and an F-measure of 71.8%) among all the combinations. Conclusion: Our finding shows that augmented user reviews can lead to better classification results, and the machine learning algorithm Bagging is more suitable for NFRs classification from user reviews than Naïve Bayes and J48.