Method for Predicting Mobile Service Evolution from User Reviews and Update Logs

Because of rapid growth in mobile application markets, competition between companies that provide similar applications has become fierce. To improve user satisfaction for keeping existing users and attracting new users, application developers need to quickly respond to customer feedback regarding functionality and performance defects. In software engineering, specifying an accurate evolution plan according to user feedback is useful but quite difficult. Hence, we propose an approach for predicting and recommending evolution plans to application developers that includes: (1) when a new version of an App should be released; (2) which features should be updated in the next version and (3) if a new version is released, to what degree users would like or dislike it. This approach is based on an elaborate text analysis of massive numbers of user reviews and App update histories. A collocation-based mRAKE method is presented to extract requested and updated features from user reviews and update logs, and the intensity and sentiment scores of each feature are calculated to quantitatively represent time-series histories of App updates and user requests. Machine learning algorithms including linear support vector, Gaussian naïve Bayes and logistic regression are employed to discover the underlying correlation between user opinions embedded in their reviews and the App update behaviors of developers, and rich experiments were conducted on real data to validate the effectiveness of the proposed approach. Overall, our approach can achieve an average accuracy of 72.8% and 93.7% in release time recommendation and content updates of successive versions, respectively, and it can predict user reactions to a planned version with an average accuracy of above 89.0%.