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

Highly Relevant Routing Recommendation Systems for Handling Few Data Using MDL Principle and Embedded Relevance Boosting Factors

A route recommendation system can provide better recommendation if it also takes collected user reviews into account, e.g. places that generally get positive reviews may be preferred. However, to classify sentiment, many classification algorithms existing today suffer in handling small data items such as short written reviews. In this paper we propose a model for a strongly relevant route recommendation system that is based on an MDL-based (Minimum Description Length) sentiment classification and show that such a system is capable of handling small data items (short user reviews). Another highlight of the model is the inclusion of a set of boosting factors in the relevance calculation to improve the relevance in any recommendation system that implements the model.Comment: ACM SIGIR 2018 Workshop on Learning from Limited or Noisy Data for Information Retrieval (LND4IR'18), July 12, 2018, Ann Arbor, Michigan, USA, 8 pages, 9 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

The evaluation of thermal hotels' online reviews

Th e main objective of this study was to evaluate the perceptions related to the online user reviews of thermal hotels. Specifi cally, it was investigated whether perceptions towards value (V), location (L), sleep quality (SQ), rooms (R), cleanliness (C), service (S) and factors infl uencing general evaluation depend on the star numbers of hotels, the location of the hotels and the nationalities of participants. In order to obtain data on perceptions of consumers towards thermal hotels in Turkey, the web site Trip Advisor (TA) was used. In total, 2,895 user reviews about thermal accommodations on TA were assessed by content analysis method. According to the study results, it was determined that the most important factor was the cleanliness of the hotels. It was followed by the location, sleep quality, rooms and service. Th e value factor was the last important. To analyse the eff ect of the nationality of the participants, domestic and foreign visitors, stars and the location of the accommodation on the perceptions towards value, location, sleep quality, rooms, cleanliness and service, t test and one-way ANOVA method were performed. It was found that the perceptions towards value, location, sleep quality, rooms, cleanliness and service diff ered between domestic and foreign visitors, nationalities, location and 4 or 5-star

The Formation of Social Influence in Online Recommendation Systems: A Study of User Reviews on Amazon.com

Thanks to Web 2.0, retail websites and online communities provide user reviews to help consumers make purchase decisions. Current IS and marketing literature reveal that user reviews can form strong social influence on consumers’ purchase decisions. However, few studies systematically examined how social influence is developed from user reviews. To bridge the gap, our research explores what factors impact the formation of social influence from user reviews. Based on a survey conducted in a controlled lab environment, the results suggest that review quality positively impacts informational influence while review consistency negatively impacts informational influence. Review consistency and social presence positively impact value-expressive influence. We also incorporate product expertise and self-monitoring as moderators into the model. Interestingly, product expertise weakens the relationship between social presence and informational influence. Self-monitoring does not impact value-expressive influence in online settings. Managerial implications are discussed