Software feature refinement prioritization based on online user review mining

Abstract Context Online software reviews have provided a wealth of user feedback on software applications. User reviews along with ratings have been influential in a series of software engineering tasks e.g. software maintenance and release planning. Objective Our research aims to assist managers in prioritizing features to be refined in next release from the perspective of enhancing user ratings via mining online reviews. Method We first extract software features from user reviews and determine their probability distribution in each review with LDA. Then the ground truth rating of each feature is estimated by linear regression under the assumption that the software functionality rating is a convex combination of all feature ratings weighted by their distribution probabilities over the review. Finally, we formalize feature refinement prioritization as an optimization problem which maximizes user group’s rating on the software functionality under the constraint of development budget. Results The proposed approach can use topic model to jointly extract features from user reviews semi-supervisedly and determine each feature’s weight in each user’s rating on the software functionality. The estimated ground truth ratings of all features reveal how reviewer group evaluate those features. Finally, we provide an illustrative example to demonstrate the key idea of our framework. Conclusion Our proposed framework is general to various software products with mass user reviews and semi-automatic without much human efforts and intervention. The framework’s interpretability helps managers better understand user feedback on the software functionality and make feature refinement plan for the upcoming releases.