User Review-Based Change File Localization for Mobile Applications

In the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespread. For instance, the integration of user feedback (provided in the form of user reviews) in the software release cycle represents a valuable asset for the maintenance and evolution of mobile apps. To fully make use of these assets, it is highly desirable for developers to establish semantic links between the user reviews and the software artefacts to be changed (e.g., source code and documentation), and thus to localize the potential files to change for addressing the user feedback. In this paper, we propose RISING (Review Integration via claSsification, clusterIng, and linkiNG), an automated approach to support the continuous integration of user feedback via classification, clustering, and linking of user reviews. RISING leverages domain-specific constraint information and semi-supervised learning to group user reviews into multiple fine-grained clusters concerning similar users' requests. Then, by combining the textual information from both commit messages and source code, it automatically localizes potential change files to accommodate the users' requests. Our empirical studies demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table

RoseMatcher: Identifying the Impact of User Reviews on App Updates

Release planning for mobile apps has recently become an area of active research. Prior research concentrated on app analysis based on app release notes in App Store, or tracking user reviews to support app evolution with issue trackers. However, as a platform for development teams to communicate with users, Apple Store has not been studied for detecting the relevance between release notes and user reviews. In this paper, we introduce RoseMatcher, an automatic approach to match relevant user reviews with app release notes, and identify matched pairs with high confidence. We collected 944 release notes and 1,046,862 user reviews from 5 mobile apps in the Apple App Store as research data, and evaluated the effectiveness and accuracy of RoseMatcher. Our evaluation shows that RoseMatcher can reach a hit ratio of 0.718 for identifying relevant matched pairs. We further conducted manual labelling and content analysis on 984 relevant matched pairs, and defined 8 roles user reviews play in app update according to the relationship between release notes and user reviews in the relevant matched pairs. The study results show that release notes tend to respond and solve feature requests, bug reports, and complaints raised in user reviews, while user reviews also tend to give positive, negative, and constructive feedback on app updates. Additionally, in the time dimension, the relevant reviews of release notes tend to be posed in a small period of time before and after the release of release notes. In the matched pairs, the time interval between the post time of release notes and user reviews reaches a maximum of three years and an average of one year. These findings indicate that the development teams do adopt user reviews when updating apps, and users show their interest in app release notes.Comment: 18 pages, 7 figure

Online User Reviews and Professional Reviews: A Bayesian Approach to Model Mediation and Moderation Effects

We propose a Bayesian analysis of mediation and moderation effects embedded within a hierarchical structure to examine the impacts of two sources of WOM information — online user reviews and professional reviews in the context of software download. Our empirical results indicate that the impact of user reviews on software download varies over time and such variation is moderated by product variety. The increase in product variety strengthens the impact of positive user reviews, while weakening the impact of negative user reviews. Furthermore, professional reviews influence software download both directly and indirectly, partially mediated by volume of online user reviews. Receiving positive professional reviews leads to more software download, yet receiving very negative professional reviews has a negative impact on the number of download. The increase in professional ratings not only directly promotes software download but also leads to more active user WOM interactions, which in turn leads to more download

Examining the Impact of User Reviews On Mobile Applications Development

User reviews were often collected to enlighten mobile applications (apps) developers on areas for improvement and novel features. However, users might not always possess the required technical expertise to make commercially feasible suggestions. The value of user reviews also varied due to their unmanageable volume and content irrelevance. In our study, over 40,000 user reviews with 50 apps would be analyzed using Python coding and regression analysis to examine the impacts of innovation and improvement led by users on apps performance in terms of revenues and user ratings. The developers’ lead time in responding to user reviews would be included as a moderator to investigate whether apps performance would be enhanced if developers respond faster. Our study should represent one of the first few attempts in offering empirical confirmation of the value of co-creation of apps with users

Do User Reviews Matter? Empirical Evidence on the Role of User Involvement in App Performance

The extant literature often presumed that user involvement was positively associated with software performance. In the context of mobile applications (apps), user reviews were collected to enlighten app developers on improvement of app quality through identifying bugs or suggesting new features. However, the value of user reviews varied a great deal due to their unmanageable volume and content irrelevance. In this study, over 40,000 user reviews with 50 apps were analyzed to empirically examine the association between customer led improvement and the revenues from the apps. Our findings indicated that customer led improvement produced significant increase in quarterly revenues. Greater growth in revenues was also observed if the developers responded to the user reviews faster. These results showed empirical support for the value of co-creation of apps with users, as customers could contribute to continuous improvement of the apps by providing experienced-based solutions