Deep Learning-Based User Feedback Classification in Mobile App Reviews

As online users are interacting with many mobile apps under different usage contexts, user needs in an app design process have become a critical issue. Existing studies indicate timely and constructive online reviews from users become extremely crucial for developers to understand user needs and create innovation opportunities. However, discovering and quantifying potential user needs from large amounts of unstructured text is a nontrivial task. In this paper, we propose a domain-oriented deep learning approach that can discover the most critical user needs such as app product new features and bug reports from a large volume of online product reviews. We conduct comprehensive evaluations including quantitative evaluations like F-measure a, and qualitative evaluations such as a case study to ensure the quality of discovered information, specifically, including the number of bug reports and feature requests. Experimental results demonstrate that our proposed supervised model outperforms the baseline models and could find more valuable information such as more important keywords and more coherent topics. Our research has significant managerial implications for app developers, app customers, and app platform providers

Enhancing users\u27 experiences with mobile app stores: What do users see? What should they see?

Using mobile applications is one of the daily habits for most smartphone users. In order to select applications, individuals need to explore the apps stores. Apps’ exploration is disturbed by the way of illustrating the applications’ information. This dissertation consists of three studies that aimed to: 1) Investigate the users’ experience with the apps’ stores; 2) Collect the users’ needs and requirements in order to have a better experience with the interface of apps’ stores; 3) Propose and evaluate a new interface design for the apps’ stores. Different types of data collection methods were administered while proceeding with the phases of this dissertation. The first study was an exploratory study, which administered an online survey, where we had102 respondents. The second study, aimed to collect the design requirements, and we interviewed 16 individuals. The third study was the interface evaluation, where we also had 35 participants. Our results showed multiple factors that affect users’ experience while discovering applications on the apps’ store. Our findings suggested that the current interface design of apps’ stores needs revisions to help users to be aware of apps’ emerging features and issues. Moreover, we found that visual cues that illustrate apps’ information would be more effective to help users perceive specific information about apps. Furthermore, visual indicators would enhance users’ knowledge regarding some of the apps’ concerns. At the end of this research, we evaluated a proposed interface design that integrates the previous design recommendations. The evaluation results illustrated positive outputs in terms of users’ satisfaction and task-completion rate. The findings indicated that participants were delighted to experience the new way of interaction with the interface of apps’ store. We anticipate that users’ experience and their awareness towards the apps issue would be improved if apps’ stores considered adopting the proposed design concept

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

Estimation of the User Contribution Rate by Leveraging Time Sequence in Pairwise Matching function-point between Users Feedback and App Updating Log

Mobile applications have become an inseparable part of people's daily life. Nonetheless, the market competition is extremely fierce, and apps lacking recognition among most users are susceptible to market elimination. To this end, developers must swiftly and accurately apprehend the requirements of the wider user base to effectively strategize and promote their apps' orderly and healthy evolution. The rate at which general user requirements are adopted by developers, or user contribution, is a very valuable metric that can be an important tool for app developers or software engineering researchers to measure or gain insight into the evolution of app requirements and predict the evolution of app software. Regrettably, the landscape lacks refined quantitative analysis approaches and tools for this pivotal indicator. To address this problem, this paper exploratively proposes a quantitative analysis approach based on the temporal correlation perception that exists in the app update log and user reviews, which provides a feasible solution for quantitatively obtaining the user contribution. The main idea of this scheme is to consider valid user reviews as user requirements and app update logs as developer responses, and to mine and analyze the pairwise and chronological relationships existing between the two by text computing, thus constructing a feasible approach for quantitatively calculating user contribution. To demonstrate the feasibility of the approach, this paper collects data from four Chinese apps in the App Store in mainland China and one English app in the U.S. region, including 2,178 update logs and 4,236,417 user reviews, and from the results of the experiment, it was found that 16.6%-43.2% of the feature of these apps would be related to the drive from the online popular user requirements