Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews

Mobile app reviews by users contain a wealth of information on the issues that users are experiencing. For example, a review might contain a feature request, a bug report, and/or a privacy complaint. Developers, users and app store owners (e.g. Apple, Blackberry, Google, Microsoft) can benefit from a better understanding of these issues – developers can better understand users’ concerns, app store owners can spot anomalous apps, and users can compare similar apps to decide which ones to download or purchase. However, user reviews are not labelled, e.g. we do not know which types of issues are raised in a review. Hence, one must sift through potentially thousands of reviews with slang and abbreviations to understand the various types of issues. Moreover, the unstructured and informal nature of reviews complicates the automated labelling of such reviews. In this paper, we study the multi-labelled nature of reviews from 20 mobile apps in the Google Play Store and Apple App Store. We find that up to 30 % of the reviews raise various types of issues in a single review (e.g. a review might contain a feature request and a bug report). We then propose an approach that can automatically assign multiple labels to reviews based on the raised issues with a precision of 66 % and recall of 65 %. Finally, we apply our approach to address three proof-of-concept analytics use case scenarios: (i) we compare competing apps to assist developers and users, (ii) we provide an overview of 601,221 reviews from 12,000 apps in the Google Play Store to assist app store owners and developers and (iii) we detect anomalous apps in the Google Play Store to assist app store owners and users.     

Leveraging app usage contexts for app recommendation: a neural approach

The large volume and variety of apps pose a great challenge for people to choose appropriate apps. As a consequence, app recommendation is becoming increasingly important. Recently, app usage data which record the sequence of apps being used by a user have become increasingly available. Such data record the usage context of each instance of app use, i.e., the app instances being used together with this app (within a short time window). Our empirical data analysis shows that a user has a pattern of app usage contexts. More importantly, the similarity in the two users’ preferences over mobile apps is correlated with the similarity in their app usage context patterns. Inspired by these important observations, this paper tries to leverage the predictive power of app usage context patterns for effective app recommendation. To this end, we propose a novel neural approach which learns the embeddings of both users and apps and then predicts a user’s preference for a given app. Our neural network structure models both a user’s preference over apps and the user’s app usage context pattern in a unified way. To address the issue of unbalanced training data, we introduce several sampling methods to sample user-app interactions and app usage contexts effectively. We conduct extensive experiments using a large real app usage data. Comparative results demonstrate that our approach achieves higher precision and recall, compared with the state-of-the-art recommendation methods.

     

Studying the consistency of star ratings and the complaints in 1 & 2-star user reviews for top free cross-platform Android and iOS apps

How users rate a mobile app via star ratings and user reviews is of utmost importance for the success of an app. Recent studies and surveys show that users rely heavily on star ratings and user reviews that are provided by other users, for deciding which app to download. However, understanding star ratings and user reviews is a complicated matter, since they are influenced by many factors such as the actual quality of the app and how the user perceives such quality relative to their expectations, which are in turn influenced by their prior experiences and expectations relative to other apps on the platform (e.g., iOS versus Android). Nevertheless, star ratings and user reviews provide developers with valuable information for improving the overall impression of their app. In an effort to expand their revenue and reach more users, app developers commonly build cross-platform apps, i.e., apps that are available on multiple platforms. As star ratings and user reviews are of such importance in the mobile app industry, it is essential for developers of cross-platform apps to maintain a consistent level of star ratings and user reviews for their apps across the various platforms on which they are available. In this paper, we investigate whether cross-platform apps achieve a consistent level of star ratings and user reviews. We manually identify 19 cross-platform apps and conduct an empirical study on their star ratings and user reviews. By manually tagging 9,902 1 & 2-star reviews of the studied cross-platform apps, we discover that the distribution of the frequency of complaint types varies across platforms. Finally, we study the negative impact ratio of complaint types and find that for some apps, users have higher expectations on one platform. All our proposed techniques and our methodologies are generic and can be used for any app. Our findings show that at least 79% of the studied cross-platform apps do not have consistent star ratings, which suggests that different quality assurance efforts need to be considered by developers for the different platforms that they wish to support.     

Studying users’ perception of IoT mobile companion apps

Internet of Things (IoT) products provide over-the-net capabilities such as remote activation, monitoring, and notifications. An associated mobile app is often provided for more convenient usage of these capabilities. The perceived quality of these companion apps can impact the success of the IoT product. We investigate the perceived quality and prominent issues of smart-home IoT mobile companion apps with the aim of deriving insights to: (i) provide guidance to end users interested in adopting IoT products; (ii) inform companion app developers and IoT producers about characteristics frequently criticized by users; (iii) highlight open research directions. We employ a mixed-methods approach, analyzing both quantitative and qualitative data. We assess the perceived quality of companion apps by quantitatively analyzing the star rating and the sentiment of 1,347,799 Android and 48,498 iOS user reviews. We identify the prominent issues that afflict companion apps by performing a qualitative manual analysis of 1,000 sampled reviews. Our analysis shows that users’ judgment has not improved over the years. A variety of functional and non-functional issues persist, such as difficulties in pairing with the device, software flakiness, poor user interfaces, and presence of issues of a socio-technical impact. Our study highlights several aspects of companion apps that require improvement in order to meet user expectations and identifies future directions.     

An empirical study of Android Wear user complaints

Wearable apps are becoming increasingly popular in recent years. Nevertheless, to date, very few studies have examined the issues that wearable apps face. Prior studies showed that user reviews contain a plethora of insights that can be used to understand quality issues and help developers build better quality mobile apps. Therefore, in this paper, we mine user reviews in order to understand the user complaints about wearable apps. We manually sample and categorize 2,667 reviews from 19 Android wearable apps. Additionally, we examine the replies posted by developers in response to user complaints. This allows us to determine the type of complaints that developers care about the most, and to identify problems that despite being important to users, do not receive a proper response from developers. Our findings indicate that the most frequent complaints are related to Functional Errors, Cost, and Lack of Functionality, whereas the most negatively impacting complaints are related to Installation Problems, Device Compatibility, and Privacy & Ethical Issues. We also find that developers mostly reply to complaints related to Privacy & Ethical Issues, Performance Issues, and notification related issues. Furthermore, we observe that when developers reply, they tend to provide a solution, request more details, or let the user know that they are working on a solution. Lastly, we compare our findings on wearable apps with the study done by Khalid et al. (2015) on handheld devices. From this, we find that some complaint types that appear in handheld apps also appear in wearable apps; though wearable apps have unique issues related to Lack of Functionality, Installation Problems, Connection & Sync, Spam Notifications, and Missing Notifications. Our results highlight the issues that users of wearable apps face the most, and the issues to which developers should pay additional attention to due to their negative impact.     

On the automatic classification of app reviews

App stores like Google Play and Apple AppStore have over 3 million apps covering nearly every kind of software and service. Billions of users regularly download, use, and review these apps. Recent studies have shown that reviews written by the users represent a rich source of information for the app vendors and the developers, as they include information about bugs, ideas for new features, or documentation of released features. The majority of the reviews, however, is rather non-informative just praising the app and repeating to the star ratings in words. This paper introduces several probabilistic techniques to classify app reviews into four types: bug reports, feature requests, user experiences, and text ratings. For this, we use review metadata such as the star rating and the tense, as well as, text classification, natural language processing, and sentiment analysis techniques. We conducted a series of experiments to compare the accuracy of the techniques and compared them with simple string matching. We found that metadata alone results in a poor classification accuracy. When combined with simple text classification and natural language preprocessing of the text—particularly with bigrams and lemmatization—the classification precision for all review types got up to 88–92 % and the recall up to 90–99 %. Multiple binary classifiers outperformed single multiclass classifiers. Our results inspired the design of a review analytics tool, which should help app vendors and developers deal with the large amount of reviews, filter critical reviews, and assign them to the appropriate stakeholders. We describe the tool main features and summarize nine interviews with practitioners on how review analytics tools including ours could be used in practice.     

Effects of the design of mobile security notifications and mobile app usability on users’ security perceptions and continued use intention

The explosive global adoption of mobile applications (i.e., apps) has been fraught with security and privacy issues. App users typically have a poor understanding of information security; worse, they routinely ignore security notifications designed to increase security on apps. By considering both mobile app interface usability and mobile security notification (MSN) design, we investigate how security perceptions of apps are formed and how these perceptions influence users’ intentions to continue using apps. Accordingly, we designed and conducted a set of controlled survey experiments with 317 participants in different MSN interface scenarios by manipulating the types of MSN interfaces (i.e., high vs. low disruption), the context (hedonic vs. utilitarian scenarios), and the degree of MSN intrusiveness (high vs. low intrusiveness). We found that both app interface usability and the design of MSNs significantly impacted users’ perceived security, which, in turn, has a positive influence on users’ intention to continue using the app. In addition, we identified an important conundrum: disruptive MSNs—a common approach to delivering MSNs—irritate users and negatively influence their perceptions of app security. Thus, our results directly challenge current practice. If these results hold, current practice should shift away from MSNs that interrupt task performance.     

一种基于支持向量机和主题模型的评论分析方法

在移动应用软件中,用户评论是一种重要的用户反馈途径.用户可能提到一些移动应用使用中的问题,比如系统兼容性问题、应用崩溃等.随着移动应用软件的广泛流行,用户提供大量无结构化的反馈评论.为了从用户抱怨评论中提取有效信息,提出一种基于支持向量机和主题模型的评论分析方法RASL（review analysis method based on SVM and LDA）以帮助开发人员更好、更快地了解用户反馈.首先对移动应用的中、差评提取特征,然后使用支持向量机对评论进行多标签分类.随后使用LDA主题模型（latent dirichlet allocation）对各问题类型下的评论进行主题提取与代表句提取.从两个移动应用中爬取5 141条用户原始评论,并对这些评论分别用RASL方法和ASUM方法进行处理,得到两个新的文本.与经典方法ASUM相比,RASL方法的困惑度更低、可理解性更佳,包含更完整的原始评论信息,冗余信息也更少.     

A transformer-based approach for improving app review response generation

Mobile apps are becoming an integral part of people's daily life by providing various functionalities, such as messaging and gaming. App developers try their best to ensure user experience during app development and maintenance to improve the rating of their apps on app platforms and attract more user downloads. Previous studies indicated that responding to users' reviews tends to change their attitude towards the apps positively. Users who have been replied are more likely to update the given ratings. However, reading and responding to every user review is not an easy task for developers since it is common for popular apps to receive tons of reviews every day. Thus, automation tools for review replying are needed. To address the need above, the paper introduces a Transformer-based approach, named TRRGen, to automatically generate responses to given user reviews. TRRGen extracts apps' categories, rating, and review text as the input features. By adapting a Transformer-based model, TRRGen can generate appropriate replies for new reviews. Comprehensive experiments and analysis on the real-world datasets indicate that the proposed approach can generate high-quality replies for users' reviews and significantly outperform current state-of-art approaches on the task. The manual validation results on the generated replies further demonstrate the effectiveness of the proposed approach.     

App store mining is not enough for app improvement

The rise in popularity of mobile devices has led to a parallel growth in the size of the app store market, intriguing several research studies and commercial platforms on mining app stores. App store reviews are used to analyze different aspects of app development and evolution. However, app users’ feedback does not only exist on the app store. In fact, despite the large quantity of posts that are made daily on social media, the importance and value that these discussions provide remain mostly unused in the context of mobile app development. In this paper, we study how Twitter can provide complementary information to support mobile app development. By analyzing a total of 30,793 apps over a period of six weeks, we found strong correlations between the number of reviews and tweets for most apps. Moreover, through applying machine learning classifiers, topic modeling and subsequent crowd-sourcing, we successfully mined 22.4% additional feature requests and 12.89% additional bug reports from Twitter. We also found that 52.1% of all feature requests and bug reports were discussed on both tweets and reviews. In addition to finding common and unique information from Twitter and the app store, sentiment and content analysis were also performed for 70 randomly selected apps. From this, we found that tweets provided more critical and objective views on apps than reviews from the app store. These results show that app store review mining is indeed not enough; other information sources ultimately provide added value and information for app developers.     

“Masculine Guys Only”: The effects of femmephobic mobile dating application profiles on partner selection for men who have sex with men

Mobile dating applications (apps) have changed the way gay men find others in their geographic area for sexual activity and romantic relationships. Many of these apps are branded in relation to traditional masculinity and have become a breeding ground for femmephobic, or anti-effeminate, language. Past research has not examined the effects of femmephobic language in social networking apps designed for men who have sex with men (MSM) on app users' perceptions. This research employed an online experiment of 143 MSM app users to test how users respond to femmephobic and non-femmephobic language use in MSM dating profiles. Participants rated the profile users, as well as reported their desire to meet the user in an offline context. Results indicated that the use of femmephobic language in dating profiles affects a potential partner's perceived intelligence, sexual confidence, and dateability, as well as one's desire to meet potential partners offline for friendship or romantic purposes. Anti-effeminacy was an important moderator of the main effect.     

What users really think about the usability of smartphone applications: diversity based empirical investigation

In recent years, smartphone devices are becoming progressively popular across a diverse range of users. However, user diversity creates challenges in smartphone application (app) development. The diversity of users is often ignored by designers and developers due to the absence of requirements. Owing to this, many smartphone users face usability issues. Despite that, no dedicated platform found that guide smartphone app designers and developers regarding human universality. The aim of this research is to explore the requirements of diverse users in smartphone apps and provide usability guidelines. The objectives of this research are achieved by following two scientific approaches. The human diversity requirements are located by conducting usability tests that investigated the requirements in the form of usability issues. The systematic literature review (SLR) process is followed in order to resolve the discovered usability issues. Both approaches resulted in a list of usability issues and guidelines. The usability tests returned 27 problems while the SLR came with a comprehensive set of universal usability guidelines that were grouped into eleven categories. The study concluded with some major outcomes. The results show evidence of critical usability problems that must be addressed during the design and development of smartphone apps. Moreover, the study also revealed that people with disabilities were three times severely affected by usability problems in such apps than people of different ages and their needs must be considered a top priority in the development of smartphone apps.

     

Why do young people use fitness apps? Cognitive characteristics and app quality

This study examined the effects of young adults’ social-cognitive characteristics and fitness apps’ quality-related characteristics on users’ intention to continue using apps. This study used a self-report questionnaire to collect data from 201 participants between November 29 and December 16, 2016. PASW/WIN 20.0 was used to perform Pearson’s correlation analysis, and hierarchical multiple regression. Results showed that users’ social-cognitive characteristics and the app’ quality-related characteristics accounted for 39.3 and 1.6% of users’ intention to continue using fitness apps, respectively. Social-cognitive characteristics included quality-related app characteristics, which explained 40.9% of users’ intention to continue using the apps. Self-efficacy, innovative propensity, outcome expectations, and engagement were key variables affecting the intention to continue using apps. Therefore, it is suggested that researchers or healthcare providers who want to utilize fitness apps for young adults could invest time and effort in the selection of existing high-quality apps and design intervention programs to stimulate users’ social-cognitive factors.     

微信小程序用户授权登录机制研究和实现

微信小程序的出现,一方面缓解了用户手机安装大量APP浪费手机存储资源并导致手机速度变慢的问题,另一方面,也减轻了开发者为不同手机操作系统(Android,iOS)分别开发程序的工作负担。微信小程序应用开发是以MVC模式的JSON作为数据交换格式的以WEB开发为基础的开发技术,但是也有很多不同于以往WEB开发的地方,尤其是用户授权登录方面,用户认证信息需要在微信小程序、开发者服务器和微信接口服务器之间传递,这个过程中要考虑用户认证信息传递的流程和数据安全问题。文章研究了这两个问题并在一个应用中做了具体实现。     

Discovering and understanding android sensor usage behaviors with data flow analysis

Today’s Android-powered smartphones have various embedded sensors that measure the acceleration, orientation, light and other environmental conditions. Many functions in the third-party applications (apps) need to use these sensors. However, embedded sensors may lead to security issues, as the third-party apps can read data from these sensors without claiming any permissions. It has been proven that embedded sensors can be exploited by well designed malicious apps, resulting in leaking users’ privacy. In this work, we are motivated to provide an overview of sensor usage patterns in current apps by investigating what, why and how embedded sensors are used in the apps collected from both a Chinese app. market called “AppChina” and the official market called “Google Play”. To fulfill this goal, We develop a tool called “SDFDroid” to identify the used sensors’ types and to generate the sensor data propagation graphs in each app. We then cluster the apps to find out their sensor usage patterns based on their sensor data propagation graphs. We apply our method on 22,010 apps collected from AppChina and 7,601 apps from Google Play. Extensive experiments are conducted and the experimental results show that most apps implement their sensor related functions by using the third-party libraries. We further study the sensor usage behaviors in the third-party libraries. Our results show that the accelerometer is the most frequently used sensor. Though many third-party libraries use no more than four types of sensors, there are still some third-party libraries registering all the types of sensors recklessly. These results call for more attentions on better regulating the sensor usage in Android apps.     

An Efficient Memory Management for Mobile Operating Systems Based on Prediction of Relaunch Distance

Recently, various mobile apps have included more features to improve user convenience. Mobile operating systems load as many apps into memory for faster app launching and execution. The least recently used (LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low. However, the LRU-based cached app termination does not distinguish between frequently or infrequently used apps. The app launch performance degrades if LRU terminates frequently used apps. Recent studies have suggested the potential of using users’ app usage patterns to predict the next app launch and address the limitations of the current least recently used (LRU) approach. However, existing methods only focus on predicting the probability of the next launch and do not consider how soon the app will launch again. In this paper, we present a new approach for predicting future app launches by utilizing the relaunch distance. We define the relaunch distance as the interval between two consecutive launches of an app and propose a memory management based on app relaunch prediction (M2ARP). M2ARP utilizes past app usage patterns to predict the relaunch distance. It uses the predicted relaunch distance to determine which apps are least likely to be launched soon and terminate them to improve the efficiency of the main memory.     

Order matters: Breaking cognitive lock-in through occasional preferential use of a rival app

Although growing attention has been paid to the use of competing technologies, little is known about how occasional preferential use of the rival system (OPUR) affects individuals’ adoption of the dominant system. In this study, we investigate how OPUR breaks the cognitive lock-in of the dominant system in the app context. The results demonstrate that supplementing OPUR reduces (increases) user access to the dominant (rival) app. In addition, the OPUR effect is more salient for high-demand and female users. Our results demonstrate that OPUR provides a great opportunity for catching-up apps to offset the first-mover advantage of dominant apps.     

A technique to circumvent SSL/TLS validations on iOS devices

SSL/TLS validations such as certificate and public key pinning can reinforce the security of encrypted communications between Internet-of-Things devices and remote servers, and ensure the privacy of users. However, such implementations complicate forensic analysis and detection of information disclosure; say, when a mobile app breaches user’s privacy by sending sensitive information to third parties. Therefore, it is crucial to develop the capacity to vet mobile apps augmenting the security of SSL/TLS traffic. In this paper, we propose a technique to bypass the system’s default certificate validation as well as built-in SSL/TLS validations performed in iOS apps. We then demonstrate its utility by analysing 40 popular iOS social networking, electronic payment, banking, and cloud computing apps.     

Seed-based authentication for mobile clients across multiple devices

Authenticating users for mobile cloud apps has been a major security issue in recent years. Traditional passwords ensure the security of mobile applications, but it also requires extra effort from users to memorize complex passwords. Seed-based authentication can simplify the process of authentication for mobile users. In the seed-based authentication, images can be used as credentials for a mobile app. A seed is extracted from an image and used to generate one-time tokens for login. Compared to complex passwords, images are more friendly to mobile users. Previous work had been done in seed-based authentication which focused on providing authentication from a single device. It is common that a mobile user may have two or more mobile devices. Authenticating the same user on different devices is challenging due to several aspects, such as maintaining the same credential for multiple devices and distinguishing different users. In this article, we aimed at developing a solution to address these issues. We proposed multiple-device authentication algorithms to identify users. We adopted a one-time token paradigm to ensure the security of mobile applications. In addition, we tried to minimize the authentication latency for better performance. Our simulation showed that the proposed algorithms can improve the average latency of authentication for 40% at most, compared to single-device solutions.     

数据驱动的移动应用用户接受度建模与预测

应用市场（app market）已经成为互联网环境下软件应用开发和交付的一种主流模式.相对于传统模式,应用市场模式下,软件的交付周期更短,用户的反馈更快,最终用户和开发者之间的联系更加紧密和直接.为应对激烈的竞争和动态演变的用户需求,移动应用开发者必须以快速迭代的方式不断更新应用,修复错误缺陷,完善应用质量,提升用户体验.因此,如何正确和综合理解用户对软件的接受程度（简称用户接受度）,是应用市场模式下软件开发需考量的重要因素.近年来兴起的软件解析学（software analytics）关注大数据分析技术在软件行业中的具体应用,对软件生命周期中大规模、多种类的相关数据进行挖掘和分析,被认为是帮助开发者提取有效信息、作出正确决策的有效途径.从软件解析学的角度,首先论证了为移动应用构建综合的用户接受度指标模型的必要性和可行性,并从用户评价数据、操作数据、交互行为数据这3个维度给出基本的用户接受度指标.在此基础上,使用大规模真实数据集,在目标用户群体预测、用户规模预测和更新效果预测等典型的用户接受度指标预测问题中,结合具体指标,提取移动应用生命周期不同阶段的重要特征,以协同过滤、回归融合、概率模型等方法验证用户接受度的可预测性,并讨论了预测结果与特征在移动应用开发过程中可能提供的指导.