Exploiting this information is however difficult due to the large number of reviews and the difficulty in extracting useful actionable information from short informal texts. App reviews -short user reviews of an app in app stores- provide a potentially rich source of information to help software development teams maintain and evolve their products. The thesis studies how mining app reviews can support software engineering.
Experiments on five popular apps indicate that SOLAR is effective for review summarization and promising for facilitating app release planning. The topic-sentiment modeling module groups the topics of the helpful reviews and also predicts the associated sentiment, and the multi-factor ranking module aims at prioritizing semantically representative reviews for each topic as the review summary. The review helpfulness prediction module assesses the helpfulness of reviews, i.e., whether the review is useful for developers. The framework mainly contains three modules: The review helpfulness prediction module, topic-sentiment modeling module, and multi-factor ranking module. In the paper, we propose a novel framework, named SOLAR, aiming at accurately summarizing helpful user reviews to developers.
Also, the new review characteristic, i.e., the number of users who rated the review as helpful, which can help capture important reviews, has not been considered previously.
There exist prior explorations on app review mining for release planning, however, most of the studies strongly rely on pre-defined classes or manually-annotated reviews. App reviews are crowdsourcing knowledge of user experience with the apps, providing valuable information for app release planning, such as major bugs to fix and important features to add.
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