App Store Research Articles

Digital exposure notification tools: A global landscape analysis.

As the COVID-19 pandemic continues, digital exposure notification systems are increasingly used to support traditional contact tracing and other preventive strategies. Likewise, a plethora of COVID-19 mobile applications have emerged. Objective: To characterize the global landscape of pandemic related mobile applications, including digital exposure notification and contact tracing tools. The following queries were entered into the Google search engine: "(*country name* COVID app) OR (COVID app *country name*) OR (COVID app *country name*+) OR (*country name*+ COVID app)". The App Store, Google Play, and official government websites were then accessed to collect descriptive data for each application. Descriptive data were qualified and quantified using standard methods. COVID-19 Exposure Notification Systems (ENS) and non-Exposure Notification products were categorized and summarized to provide a global landscape review. Our search resulted in a global count of 224 COVID-19 mobile applications, in 127 countries. Of these 224 apps, 128 supported exposure notification, with 75 employing the Google Apple Exposure Notification (GAEN) application programming interface (API). Of the 75 apps using the GAEN API, 15 apps were developed using Exposure Notification Express, a GAEN turnkey solution. COVID-19 applications that did not include exposure notifications (n = 96) focused on COVID-19 Self-Assessment (35·4%), COVID-19 Statistics and Information (32·3%), and COVID-19 Health Advice (29·2%). The digital response to COVID-19 generated diverse and novel solutions to support non-pharmacologic public health interventions. More research is needed to evaluate the extent to which these services and strategies were useful in reducing viral transmission.

RoseMatcher: Identifying the impact of user reviews on app updates

The release planning of mobile apps has become an area of active research, with most studies centering on app analysis through release notes in the Apple App Store and tracking user reviews via issue trackers. However, the correlation between these release notes and user reviews in App Store remains understudied. In this paper, we introduce RoseMatcher, a novel 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 to evaluate the effectiveness and accuracy of RoseMatcher, and conducted deep content analysis on matched pairs. Our evaluation shows that RoseMatcher can reach a hit ratio of 0.718 for identifying relevant matched pairs, and with the manual labeling and content analysis of 984 relevant pairs, we identify 8 roles that user reviews play in app updates according to the relationship between release notes and user reviews in the relevant matched pairs. Our findings indicate that both app development teams and users pay close attention to release notes and user reviews, with release notes typically addressing feature requests, bug reports, and complaints, and user reviews offering positive, negative, and constructive feedback. Overall, the study highlights the importance of the communication between app development teams and users in the release planning of mobile apps, with relevant reviews tending to be posed within a short period before and after the release of release notes, with the average time interval between the post time of release notes and user reviews being approximately one year.

Sentiment Analysis of Indonesian TikTok Review Using LSTM and IndoBERTweet Algorithm

TikTok is currently the most popular app in the world and thus gets many reviews on the Google Play Store and other app marketplace platforms. These reviews are valuable user opinions that can be analyzed further for many purposes. Harnessing valuable analyses from these reviews can be obtained manually, which will be time-consuming and costly, or automatically with machine learning methods. This paper implements the latter with LSTM and IndoBERTweet, a derivative of BERT, using Indonesian vocabulary from Twitter post data. This research aims to determine the appropriate method to create a model that can automatically classify TikTok reviews into negative, neutral, and positive sentiments. The result demonstrates that IndoBERTweet outperforms the other, with an accuracy of 80%, whereas the LSTM accuracy is at 78%.

Stress Management Apps: Systematic Search and Multidimensional Assessment of Quality and Characteristics.

Chronic stress poses risks for physical and mental well-being. Stress management interventions have been shown to be effective, and stress management apps (SMAs) might help to transfer strategies into everyday life. This review aims to provide a comprehensive overview of the quality and characteristics of SMAs to give potential users or health professionals a guideline when searching for SMAs in common app stores. SMAs were identified with a systematic search in the European Google Play Store and Apple App Store. SMAs were screened and checked according to the inclusion criteria. General characteristics and quality were assessed by 2 independent raters using the German Mobile Application Rating Scale (MARS-G). The MARS-G assesses quality (range 1 to 5) on the following four dimensions: (1) engagement, (2) functionality, (3) esthetics, and (4) information. In addition, the theory-based stress management strategies, evidence base, long-term availability, and common characteristics of the 5 top-rated SMAs were assessed and derived. Of 2044 identified apps, 121 SMAs were included. Frequently implemented strategies (also in the 5 top-rated SMAs) were psychoeducation, breathing, and mindfulness, as well as the use of monitoring and reminder functions. Of the 121 SMAs, 111 (91.7%) provided a privacy policy, but only 44 (36.4%) required an active confirmation of informed consent. Data sharing with third parties was disclosed in only 14.0% (17/121) of the SMAs. The average quality of the included apps was above the cutoff score of 3.5 (mean 3.59, SD 0.50). The MARS-G dimensions yielded values above this cutoff score (functionality: mean 4.14, SD 0.47; esthetics: mean 3.76, SD 0.73) and below this score (information: mean 3.42, SD 0.46; engagement: mean 3.05, SD 0.78). Most theory-based stress management strategies were regenerative stress management strategies. The evidence base for 9.1% (11/121) of the SMAs could be identified, indicating significant group differences in several variables (eg, stress or depressive symptoms) in favor of SMAs. Moreover, 38.0% (46/121) of the SMAs were no longer available after a 2-year period. The moderate information quality, scarce evidence base, constraints in data privacy and security features, and high volatility of SMAs pose challenges for users, health professionals, and researchers. However, owing to the scalability of SMAs and the few but promising results regarding their effectiveness, they have a high potential to reach and help a broad audience. For a holistic stress management approach, SMAs could benefit from a broader repertoire of strategies, such as more instrumental and mental stress management strategies. The common characteristics of SMAs with top-rated quality can be used as guidance for potential users and health professionals, but owing to the high volatility of SMAs, enhanced evaluation frameworks are needed.

Comparison of Naïve Bayes and Logistic Regression in Sentiment Analysis on Marketplace Reviews Using Rating-Based Labeling

This research focuses on sentiment analysis in the marketplace reviews in Google Play Store, a platform for downloading Android applications and providing reviews. Sentiment analysis is essential for understanding user responses to applications, particularly in the app marketplace. In this study, two machine learning algorithms, Naïve Bayes and Logistic Regression, are employed to classify user reviews. The application rating is used as a reference to determine the sentiment of each comment. The dataset is divided into two conditions: using 2 labels (positive & negative) and 3 labels (positive, neutral, & negative). The test results indicate that the highest performance is achieved by classifying with Logistic Regression on the Shopee dataset with 2 labels. The accuracy reaches 84.58%, precision reaches 84.66%, and recall reaches 84.63%. Additionally, the fastest processing time occurs when testing the Lazada 2-label dataset with Naïve Bayes, taking only 0.038 seconds. Overall, the research suggests that datasets with 2 labels tend to yield higher accuracy compared to datasets with 3 labels.

Propose technology and management model of mobile cloud computing for the Vietnam national digital transformation program

Cloud computing is considered a digital platform to develop mobile applications for the National Digital Transformation Program 2025 with a vision to 2030. There are many solutions to build a cloud computing platform, but those solutions consume hardware resources, high communication latency and lack of mobility, thus increasing the cost of investment in building a cloud computing platform. To address the disadvantages of the above solutions, this study proposes a mobile cloud computing model aimed at providing services that only implement on mobile devices, enhancing the calculation capacity on mobile devices, minimising resource shortages and performing efficiently for resource-intensive mobile applications. To do that, the solution proposed in this study is to enable the use of the nearest resource-rich mobile devices that can provide computing services to mobile users in the vicinity. Utilising leisure resources to serve resource-intensive mobile applications helps the government to reduce hardware investment for building cloud computing platforms. In addition, this study also proposes a management model for mobile applications through the App Store that is owned and managed by the Vietnamese government. From there, the government will monitor mobile applications and not let them develop spontaneously, causing difficulties and loss of trust for users.

Exploring the privacy concerns of smartphone app users: a qualitative approach

PurposeThe purpose of this study is to explore and identify the privacy concerns of smartphone app users pertinent to app usage.Design/methodology/approachAdopting a qualitative phenomenological approach, the authors conducted semi-structured interviews with app users to explore the app users' privacy concerns.FindingsCredibility concerns, unauthorised secondary use and vulnerability concerns are the three major privacy concerns of app users, under which these concerns have sub-concerns, i.e. popularity, privacy policy, stalking, data sharing, hacking and personal harm.Practical implicationsThe findings are useful to app marketers, app developers and app stores. App marketers, app developers and app stores can use the findings to understand and properly address app users' privacy concerns, thereby increasing the apps usage.Originality/valueBy exploring the privacy concerns of app users, the authors' study extends the literature and provides a theoretical development of individuals' privacy concerns in the context of a widely used technology, i.e. smartphone applications. Accordingly, this study contributes to the consumer privacy literature.

Flower-Insect Timed Counts (FIT Count): Protocol Adaptation and Preliminary Results in Brazil

FIT Count (Flower-Insect Timed Counts) is a citizen science initiative that seeks to monitor flower visitation by diverse pollinator groups, including bumblebees, stingless bees, honeybees, flies, hummingbirds, among others. The protocol entails determining a 50 x 50 cm plot area around a target plant species, photographing the plant and conducting a standardized 10-minute survey on this area to estimate the frequency of flower visits by different pollinator groups. Conducting FIT Counts in different habitats and locations produces data on the temporal and spatial dynamics of these interactions (Carvell 2022, UK Pollinator Monitoring Scheme 2018). FIT Count methodology was originally developed by the UK Pollinator Monitoring Scheme (PoMS) and has been used in the United Kingdom since 2017, with over 8,500 counts conducted to date. In 2021, the FIT Count application (app) was launched in the United Kingdom, and became available in Brazil, Chile, Argentina, and some other European countries in 2022. After installation, users may select in which country the observations will be conducted and their preferred language, either English or the main native language of participating countries. The adaptation of the application for use by Brazilian citizen scientists involved not only the translation of the interface, but also relied on the expertise of a local team who helped select which plant species and pollinator groups would be appropriate and representative of biodiversity within the country (Fig. 1). The application is supported by a website that features a dedicated page for each country. Users can download the app on Google Play or App Store (Fig. 2). To allow the participation of non-experts, who generally are not familiar with taxonomy or species identification, the flower visitors are identified in general categories such as bumblebees and carpenter bees (grouped together), flies, and others (see Fig. 1), representing the main functional groups of pollinators. If a flower visitor cannot be identified, it should be classified as "other insect" or marked as "I don't know". Including the possibility of uncertainty in flower visitor identification was a suggestion of the Brazilian team for quality assurance during data collection. Currently, 834 flower visits have been counted by 15 participants, who performed 109 FIT Counts across Brazil as a pilot testing phase of the app. Two training workshops were held to disseminate the app, and an illustrated guide was published to help participants identify flower types and flower visitor groups (Koffler 2022). Nineteen plant species were monitored, but most flower-visits counted were on basil (Ocimum basillicum, n = 47) and false heather (Cuphea hyssopifolia, n = 33). The mean number of flower visitors per FIT Count was 8 (min = 0, max = 45). While most visits were performed by honeybees, participants also recorded visits by beetles, butterflies or moths, small insects, solitary bees, wasps and other insects. Next steps include assessing data quality and promoting strategic partnerships to further disseminate this citizen science initiative. For instance, quality control measures may include assessing whether participants perform the protocol steps according to instructions and whether plants and flower visitors are correctly identified and counted. Also, we are exploring the prospective implementation of the Darwin Core standard (Darwin Core Task Group 2009) along with the Plant-Pollinator Interactions vocabulary (Salim 2022) to standardize data description.

Aplikasi Analisis Sentimen Komentar Pengguna Genshin Impact Di Play Store

Google Play functions as the official app store for the Android operating system, allowing users to browse and discover applications developed with the Android software development kit (SDK) and published through Google. Google Play also serves as a digital media store, offering music programs, books, movies, and television shows. It previously offered Google hardware for purchase until it introduced a separate online hardware store, Google Store, on March 11, 2015.The research will utilize a web-based application development tool that uses Flask and JavaScript as the application interface, and the Pandas library from Python for data manipulation. Naïve Bayes will be employed as the methodology for analyzing sentiments based on words, and K-Fold Cross Validation will be used to strengthen the accuracy of the analysis results.Sentiment analysis typically classifies opinions into three categories: positive and negative. However, applications that can perform the process of creating training and testing sets from consumer opinion data, simultaneously analyzing consumer sentiment and dynamically measuring the accuracy of the analysis results, are still scarce. This study aims to develop an application capable of analyzing consumer sentiment with the mentioned functionalities, wherein Naive Bayes is used as the classification method.

A STUDY ON CUSTOMER SATISFACTION ON SBI YONO WITH REFERENCE TO STONEHOUSE PET, NELLORE

In India, Banking industries has elaborated various financial rearrangements that would createa shift from all the traditional banking to digitalized banking services to gratify the customer’srequirement. Mobile banking increasing rapidly in India because customers find it more easy and comfortable way for doing banking transactions. Thus digitalized banking services has taken a step further in its digital platform through applications to access account information and other services provided by banks. This type of digitalized banking services will diminish branch level transaction and in due course of time it will make ease for customer services pressure, thus declines the operating cost for banks. SBI provides a lot of applications can be downloaded to the smart phones from app store and are free to experience the freedom to access anywhere anytime the banking services. SBI has several applications for their customers for ease transactions such as SBI ANYWHERE, SBI E-PAY, SBI QUICK, SBI BUDDY and SBIYONO. YONO is the latest application of SBI where the user’s savings bank account can be opened digitally using new YONO app and portal SBI. YONO is new app launched by State Bank of India.

Assessing User Experience and Usability in the OVO Application: Utilizing the User Experience Questionnaire and System Usability Scale for Evaluation

The OVO application, despite having a large user base in Indonesia, has received low ratings compared to other digital wallet apps on the Google Play Store and App Store. Users frequently complain about the user experience, which greatly affects their overall satisfaction. This study evaluates the user experience and usability of the OVO application using the User Experience Questionnaire (UEQ) and System Usability Scale (SUS). The UEQ results show that efficiency is excellent (1.55), while attractiveness, perspicuity, dependability, and stimulation are above average (1.56, 1.67, 1.33, and 1.16, respectively). However, the novelty aspect falls below average (0.64), indicating a need for improvement. The SUS score is 77.53, classifying the application as "Acceptable" with a "C" grade and an overall "Good" rating. Addressing identified shortcomings can enhance user experience and usability, ultimately improving user satisfaction. This study contributes valuable empirical data to the field, offering insights for researchers and practitioners in assessing the user experience and usability of mobile applications.

Toward an autism-friendly environment based on mobile apps user feedback analysis using deep learning and machine learning models.

Autistic people are often disadvantaged in employment, education, etc. In fact, autistic students/employees face several challenges navigating and communicating with their superiors and colleagues. Mobile applications for people with Autism Spectrum Disorder (ASD apps for short) have been increasingly being adapted to help autistic people manage their conditions and daily activities. User feedback analysis is an effective method that can be used to improve ASD apps' services. In this article, we investigate the usage of ASD apps to improve the quality of life for autistic students/employees based on user feedback analysis. For this purpose, we analyze user reviews suggested on highly ranked ASD apps for college students, and workers. A total of 97,051 reviews have been collected from 13 ASD apps available on Google Play and Apple App stores. The collected reviews have been classified into negative, positive, and neutral opinions. This analysis has been performed using machine learning and deep learning models. The best performances were provided by combining RNN and LSTM models with an accuracy of 96.58% and an AUC of 99.41%. Finally, we provide some recommendations to improve ASD apps to assist developers in upgrading the main services provided by their apps.

Identification of the Most Suitable Mobile Apps to Support Dietary Approaches to Stop Hypertension (DASH) Diet Self-Management: Systematic Search of App Stores and Content Analysis.

Smartphone apps might provide an opportunity to support the Dietary Approaches to Stop Hypertension (DASH) diet, a healthy diet designed to help lower blood pressure. This study evaluated DASH diet self-management apps based on their quality, likely effectiveness, and data privacy/security to identify the most suitable app(s). A systematic search and content analysis were conducted of all DASH diet apps available in Google Play and the Apple App Store in the UK in November 2022. Apps were included if they provided DASH diet tracking. A previous systematic literature review found some commercial apps not found in the app store search, and these were also included in this review. Three reviewers used the App Quality Evaluation Tool (AQEL) to assess each app's quality across seven domains: knowledge acquisition, skill development, behaviour change, purpose, functionality, and appropriateness for adults with hypertension. Domains with a score of 8 or higher were considered high-quality. Two reviewers assessed the apps' data privacy and security and then coded Behaviour change techniques (BCTs) linked to the Theoretical Domain Framework (TDF) underpinning the likely effectiveness of the apps. Seven DASH diet apps were assessed, showing the limited availability of apps supporting DASH diet self-management. The AQEL assessment showed that three apps scored higher than eight in most of the AQEL domains. Nineteen BCTs were used across the apps, linked to nine TDF action mechanisms that may support DASH diet self-management behaviours. Four apps met standards for privacy and security. All seven apps with self-monitoring functionality had sufficient theoretical basis to demonstrate likely effectiveness. However, most had significant quality and data security shortcomings. Only two apps, NOOM and DASH To TEN, were found to have both adequate quality and security and were thus deemed suitable to support DASH diet self-management.

HELPERKU: INNOVATIVE E-COMMERCE APPLICATIONS AS BUSINESS SOLUTIONS FOR PEOPLE WITH DISABILITIES

HELPERKU is an innovative e-commerce application where product sales carried out by Persons with Disabilities are designed with 4.0-based display device features. HELPERKU is here to provide convenience for everyone who has limitations but wants to start a business or already has a business to be able to gain knowledge and share experiences about their business through attractive supporting features. The strategy analysis tools used are SWOT Analysis, PESTLE, 4P, and Five Force’s Porter Analysis. HELPERKU will be downloaded via Google Play Store and App Store, making it easier for mobile/android users. Jakarta is the first location for HELPERKU to operate its application. The presence of HELPERKU is expected to become a large application company that can be useful for users, especially people with disabilities, investors, as well as a medium for experimenting in terms of knowledge, practical learning, and affordable business consulting with the presence of experienced experts and figures in the field of business. It is hoped that after the HELPERKU application, people who want to start a business can experience business expansion in a relatively short time. HELPERKU is still a relatively new application, but HELPERKU opens opportunities for investors to invest with collateral in a relatively short time, which is two years and five months with an annual discount rate of 7% and return on investment of 20%. HELPERKU earnings during this period are normal for new app companies.

HELPERKU: INNOVATIVE E-COMMERCE APPLICATIONS AS BUSINESS SOLUTIONS FOR PEOPLE WITH DISABILITIES

HELPERKU is an innovative e-commerce application where product sales carried out by Persons with Disabilities are designed with 4.0-based display device features. HELPERKU is here to provide convenience for everyone who has limitations but wants to start a business or already has a business to be able to gain knowledge and share experiences about their business through attractive supporting features. The strategy analysis tools used are SWOT Analysis, PESTLE, 4P, and Five Force’s Porter Analysis. HELPERKU will be downloaded via Google Play Store and App Store, making it easier for mobile/android users. Jakarta is the first location for HELPERKU to operate its application. The presence of HELPERKU is expected to become a large application company that can be useful for users, especially people with disabilities, investors, as well as a medium for experimenting in terms of knowledge, practical learning, and affordable business consulting with the presence of experienced experts and figures in the field of business. It is hoped that after the HELPERKU application, people who want to start a business can experience business expansion in a relatively short time. HELPERKU is still a relatively new application, but HELPERKU opens opportunities for investors to invest with collateral in a relatively short time, which is two years and five months with an annual discount rate of 7% and return on investment of 20%. HELPERKU earnings during this period are normal for new app companies.

Compassion apps for better mental health: qualitative review.

There is increasing empirical evidence for the positive mental health effects of compassion-based interventions. Although numerous smartphone apps offering compassion-based interventions ('compassion apps') are now available for the general public, the quality of these apps has not yet been reviewed. A qualitative review of existing compassion apps serves as a crucial first step toward testing the efficacy of these apps, by identifying good-quality compassion apps that might be worth the investment of a scientific trial. The current study focuses on reviewing the quality of existing compassion apps. Existing compassion apps were identified through searches in the Google Play Store and App Store. The 24 included apps were reviewed on their quality by using the Mobile App Rating Scale, and on their consistency with current evidence by comparing them to existing and studied compassion-based interventions. Of the 24 included apps, eight were identified that met the criteria of being consistent with existing and studied compassion-based interventions, and acceptable to good overall quality. The other 16 apps failed to meet one or both of these criteria. Good-quality compassion apps are available, but many of the available apps fail to meet certain quality criteria. In particular, many apps failed to offer sufficient relevant and correct information, or failed to offer this information in an entertaining and interesting way. It is recommended that future compassion apps are based on a clear definition of compassion, offer evidence- and theory-based exercises and implement tools for increasing engagement.

Chronic kidney disease and mobile health: quality of renal nutritional APPs in Italy.

Patient with chronic kidney disease (CKD) must adhere to a nutritional therapy characterized by a restrictive dietary scheme. Nutritional self-care can be enhanced through the use of nutritional apps. The purpose of this study is to evaluate the characteristics of specific nutritional apps for chronic renal failure available in Italy. A systematic search of mobile apps was conducted by two academic researchers in three Italian App stores: Google Play Store, Apple Store and Huawei AppGallery. Of the 1602 apps identified, 2 apps (Miku; MyFIR) were evaluated with the Italian version of a Mobile Application Rating Scale (MARS-ITA) by a multidisciplinary team of 20 professionals. The study found that the two selected apps, available in the Google Play Store and Apple Store, aim to increase well-being through the acquisition of knowledge and behavioral change; but none identify health goals that should be achieved. The strategies used by the two applications are: information, education, monitoring and cognitive-behavioral challenge. The technical analysis showed adequate protection of personal data but only the most downloaded app (Miku) provides the possibility to share content, to send reminders and to browse when offline. The main Italian app stores have two applications to monitor physical health and nutrition in patients with renal disease. Although these are free, easily accessible, navigable and valid in terms of health monitoring and information, greater availability of the offer would be desirable.

Participants' Underlying Beliefs of Using WIC Electronic Benefit Transfer (EBT) Cards in Stores in New Jersey.

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) is an essential nutrition assistance program that has led to successful health outcomes and healthcare access. To alleviate discomfort associated with WIC shopping at stores, the Congress mandated the transition to electronic benefit transfer (EBT) card system from paper vouchers. This study aimed to explore the experiences of WIC recipients in using EBT cards in stores. WIC recipients from one WIC clinic in northern New Jersey (N = 220) participated in this study. An online survey was framed under the theory of planned behavior to probe their underlying behavioral, normative, and control beliefs of using EBT cards. Using content analyses, dominating themes of each belief were extracted. Participants composed of Hispanic (91.2%) with mean age of 31.68 (SD = 7.69). Most frequently mentioned advantages of using EBT cards were convenience and ease (34.3%) followed by fast and efficient (28.5%). Participants noted that most people would approve of using EBT cards (70%), especially those who receive the benefits and approve of the public assistance (16.7%). They stated that having improved store inventory and an increase in WIC-authorized stores (17.2%) would make the EBT cards use easy. The transition to EBT cars allowed purchase flexibility, alleviated stigma and the purchase process burdens, but the challenges associated with WIC app usage and store specific issues remained. These challenges should be addressed in future intervention to enable WIC recipients more engaged in using the EBT cards.

A Facile Approach To Represent Neural Network With Less Storage Space

Deep neural networks have been applied to different applications, such as image recognition, data analysis, natural language processing, and so on. Some companies regularly release their mobile applications to app stores. Usually, app stores have a restriction that overweight applications can not be downloaded until the smartphone connects to Wi-Fi. Unfortunately, the size of the application that consists of deep neural networks may be too large. In order to reduce the size of application, an efficient compression approach is highly desired. An efficient encoding for compressed neural networks is a critical point for reducing storage complexity. Most of previous studies focus on the sparse matrix encoding instead of the dense one. The dense matrix encoding methods have not been studied well. Meanwhile, sparse matrix encoding can not attain a high compression rate when encoding a dense matrix. Toward this end, we proposed an alternative compression algorithm that linearizes the weights on each layer and then stores linearized weights with critical information. For a dense matrix, our algorithm can achieve a higher compression rate than one existing sparse matrix encoding. Experiment results indicate that our proposed piecewise linearization scheme can make VGG-16, Resnet152, and Densenet169 achieve at least two times of the compression rates. In other words, neural networks parameters can be stored in 40–50% of the original space.

STRE: An Automated Approach to Suggesting App Developers When to Stop Reading Reviews

It is well known that user feedback ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , reviews) plays an essential role in mobile app maintenance. Users upload their troubles, app issues, or praises, to help developers refine their apps. However, reading tremendous amounts of reviews to retrieve useful information is a challenging job. According to our manual studies, reviews are full of repetitive opinions, thus developers could stop reading reviews when no more new helpful information appears. Developers can extract useful information from partial reviews to ameliorate their app and then develop a new version. However, it is tough to have a good trade-off between getting enough useful feedback and saving more time. In this paper, we propose a novel approach, named STRE, which utilizes historical reviews to suggest the time when most of the useful information appears in reviews of a certain version. We evaluate STRE on 62 recent versions of five apps from Apple's App Store. Study results demonstrate that our approach can help developers save their time by up to 98.33% and reserve enough useful reviews before stopping to read reviews such that developers do not spend additional time in reading redundant reviews over the suggested stopping time. At the same time, STRE can complement existing review categorization approaches that categorize reviews to further assist developers. In addition, we find that the missed top-word-related reviews appearing after the suggested stopping time contain limited useful information for developers. Finally, we find that 12 out of 13 of the emerging bugs from the studied versions appear before the suggested stopping time. Our approach demonstrates the value of automatically refining information from reviews.