An empirical study of emergency updates for top android mobile apps

The mobile app market continues to grow at a tremendous rate. The market provides a convenient and efficient distribution mechanism for updating apps. App developers continuously leverage such mechanism to update their apps at a rapid pace. The mechanism is ideal for publishing emergency updates (i.e., updates that are published soon after the previous update). In this paper, we study such emergency updates in the Google Play Store. Examining more than 44,000 updates of over 10,000 mobile apps in the Google Play Store, we identify 1,000 emergency updates. By studying the characteristics of such emergency updates, we find that the emergency updates often have a long lifetime (i.e., they are rarely followed by another emergency update). Updates preceding emergency updates often receive a higher ratio of negative reviews than the emergency updates. However, the release notes of emergency updates rarely indicate the rationale for such updates. Hence, we manually investigate the binary changes of several of these emergency updates. We find eight patterns of emergency updates. We categorize these eight patterns along two categories “Updates due to deployment issues” and “Updates due to source code changes”. We find that these identified patterns of emergency updates are often associated with simple mistakes, such as using a wrong resource folder (e.g., images or sounds) for an app. We manually examine each pattern and document its causes and impact on the user experience. App developers should carefully avoid these patterns in order to improve the user experience.     

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.     

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.     

Forensic taxonomy of android productivity apps

Android productivity apps have provided the facility of having a constantly accessible and productive workforce to the information and work capabilities needed by the users. With hundreds of productivity apps available in the Android app market, it is necessary to develop a taxonomy for the forensic investigators and the end users to allow them to know what personal data remnants are available from the productivity apps. In this paper, 30 popular Android productivity apps were examined. A logical extraction of the Android phone was collected by using a well-known mobile forensic tool- XRY to extract various information of forensic interest such as user email ID and list of tasks. Based on the findings, a two-dimensional taxonomy of the forensic artefacts of the productivity apps is proposed with the app categories in one dimension and the classes of artefacts in the other dimension. The artefacts identified in the study of the apps are summarised using the taxonomy. In addition, a comparison with the existing forensic taxonomies of different categories of Android apps is provided to facilitate timely collection and analysis of evidentiary materials from mobile devices.     

Effort-aware cross-project just-in-time defect prediction framework for mobile apps

As the boom of mobile devices, Android mobile apps play an irreplaceable roles in people’s daily life, which have the characteristics of frequent updates involving in many code commits to meet new requirements. Just-in-Time (JIT) defect prediction aims to identify whether the commit instances will bring defects into the new release of apps and provides immediate feedback to developers, which is more suitable to mobile apps. As the within-app defect prediction needs sufficient historical data to label the commit instances, which is inadequate in practice, one alternative method is to use the cross-project model. In this work, we propose a novel method, called KAL, for cross-project JIT defect prediction task in the context of Android mobile apps. More specifically, KAL first transforms the commit instances into a high-dimensional feature space using kernel-based principal component analysis technique to obtain the representative features. Then, the adversarial learning technique is used to extract the common feature embedding for the model building. We conduct experiments on 14 Android mobile apps and employ four effort-aware indicators for performance evaluation. The results on 182 cross-project pairs demonstrate that our proposed KAL method obtains better performance than 20 comparative methods.     

Field experience with obfuscating million-user iOS apps in large enterprise mobile development

In recent years, mobile apps have become the infrastructure of many popular Internet services. It is now common that a mobile app serves millions of users across the globe. By examining the code of these apps, reverse engineers can learn various knowledge about the design and implementation of the apps. Real-world cases have shown that the disclosed critical information allows malicious parties to abuse or exploit the app-provided services for unrightful profits, leading to significant financial losses. One of the most viable mitigations against malicious reverse engineering is to obfuscate the apps. Despite that security by obscurity is typically considered to be an unsound protection methodology, software obfuscation can indeed increase the cost of reverse engineering, thus delivering practical merits for protecting mobile apps. In this paper, we share our experience of applying obfuscation to multiple commercial iOS apps, each of which has millions of users. We discuss the necessity of adopting obfuscation for protecting modern mobile business, the challenges of software obfuscation on the iOS platform, and our efforts in overcoming these obstacles. We especially focus on factors that are unique to mobile software development that may affect the design and deployment of obfuscation techniques. We report the outcome of our obfuscation with empirical experiments. We additionally elaborate on the follow-up case studies about how our obfuscation affected the app publication process and how we responded to the negative impacts. This experience report can benefit mobile developers, security service providers, and Apple as the administrator of the iOS ecosystem.     

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.     

A study of the relation of mobile device attributes with the user-perceived quality of Android apps

The number of mobile applications (apps) and mobile devices has increased considerably over the past few years. Online app markets, such as the Google Play Store, use a star-rating mechanism to quantify the user-perceived quality of mobile apps. Users may rate apps on a five point (star) scale where a five star-rating is the highest rating. Having considered the importance of a high star-rating to the success of an app, recent studies continue to explore the relationship between the app attributes, such as User Interface (UI) complexity, and the user-perceived quality. However, the user-perceived quality reflects the users’ experience using an app on a particular mobile device. Hence, the user-perceived quality of an app is not solely determined by app attributes. In this paper, we study the relation of both device attributes and app attributes with the user-perceived quality of Android apps from the Google Play Store. We study 20 device attributes, such as the CPU and the display size, and 13 app attributes, such as code size and UI complexity. Our study is based on data from 30 types of Android mobile devices and 280 Android apps. We use linear mixed effect models to identify the device attributes and app attributes with the strongest relationship with the user-perceived quality. We find that the code size has the strongest relationship with the user-perceived quality. However, some device attributes, such as the CPU, have stronger relationships with the user-perceived quality than some app attributes, such as the number of UI inputs and outputs of an app. Our work helps both device manufacturers and app developers. Manufacturers can focus on the attributes that have significant relationships with the user-perceived quality. Moreover, app developers should be careful about the devices for which they make their apps available because the device attributes have a strong relationship with the ratings that users give to apps.     

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.     

Detecting plagiarized mobile apps using API birthmarks

This paper addresses the problem of detecting plagiarized mobile apps. Plagiarism is the practice of building mobile apps by reusing code from other apps without the consent of the corresponding app developers. Recent studies on third-party app markets have suggested that plagiarized apps are an important vehicle for malware delivery on mobile phones. Malware authors repackage official versions of apps with malicious functionality, and distribute them for free via these third-party app markets. An effective technique to detect app plagiarism can therefore help identify malicious apps. Code plagiarism has long been a problem and a number of code similarity detectors have been developed over the years to detect plagiarism. In this paper we show that obfuscation techniques can be used to easily defeat similarity detectors that rely solely on statically scanning the code of an app. We propose a dynamic technique to detect plagiarized apps that works by observing the interaction of an app with the underlying mobile platform via its API invocations. We propose API birthmarks to characterize unique app behaviors, and develop a robust plagiarism detection tool using API birthmarks.     

Status and trends of mobile-health applications for iOS devices: A developer's perspective

Modern smart mobile devices offer media-rich and context-aware features that are highly useful for electronic-health (e-health) applications. It is therefore not surprising that these devices have gained acceptance as target devices for e-health applications, turning them into m-health (mobile-health) apps. In particular, many e-health application developers have chosen Apple's iOS mobile devices such as iPad, iPhone, or iPod Touch as the target device to provide more convenient and richer user experience, as evidenced by the rapidly increasing number of m-health apps in Apple's App Store. In this paper, the top two hundred of such apps from the App Store were examined from a developer's perspective to provide a focused overview of the status and trends of iOS m-health apps and an analysis of related technology, architecture, and user interface design issues. The top 200 apps were classified into different groups according to their purposes, functions, and user satisfaction. It was shown that although the biggest group of apps was medical information reference apps that were delivered from or related to medical articles, websites, or journals, mobile users disproportionally favored tracking tools. It was clear that m-health apps still had plenty of room to grow to take full advantage of unique mobile platform features and truly fulfill their potential. In particular, introduction of two- or three-dimensional visualization and context-awareness could further enhance m-health app's usability and utility. This paper aims to serve as a reference point and guide for developers and practitioners interested in using iOS as a platform for m-health applications, particular from the technical point of view.     

An analysis of the relationship between quality and user acceptance in smartphone apps

This study investigates consumer intentions within the smartphone app environment. More specifically, it studies the factors influencing the intention to use banking apps based on the smartphone by employing the information system success model and a revised technology acceptance model. The study examines how quality factors and attitudes toward mobile apps-based banking influence the intention to use banking apps, and whether trust influences the relationship between quality factors and intention to use. In it, we collect data from 520 users and estimate the structural model. The results indicate that attitudes to mobile apps-based banking, as well as information and service quality, affect consumers’ intention to use banking apps. We further confirm that three particular quality factors, moderated by trust, affect the intention to use these apps. This study helps to explain consumers’ mobile apps-based banking behaviours, by combining the information system success model with a technology acceptance model.     

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.     

Is Sampling Useful in Data Mining? A Case in the Maintenance of Discovered Association Rules

By nature, sampling is an appealing technique for data mining, because approximate solutions in most cases may already be of great satisfaction to the need of the users. We attempt to use sampling techniques to address the problem of maintaining discovered association rules. Some studies have been done on the problem of maintaining the discovered association rules when updates are made to the database. All proposed methods must examine not only the changed part but also the unchanged part in the original database, which is very large, and hence take much time. Worse yet, if the updates on the rules are performed frequently on the database but the underlying rule set has not changed much, then the effort could be mostly wasted. In this paper, we devise an algorithm which employs sampling techniques to estimate the difference between the association rules in a database before and after the database is updated. The estimated difference can be used to determine whether we should update the mined association rules or not. If the estimated difference is small, then the rules in the original database is still a good approximation to those in the updated database. Hence, we do not have to spend the resources to update the rules. We can accumulate more updates before actually updating the rules, thereby avoiding the overheads of updating the rules too frequently. Experimental results show that our algorithm is very efficient and highly accurate.     

Dynamically Updating XML Data: Numbering Scheme Revisited

Almost all existing approaches use certain numbering scheme to encode XML elements to facilitate query processing when XML data is stored in databases. For example, under the most popular region-based numbering scheme, the starting and ending positions of an element in a document are used as the code to identify the element so that the ancestor/descendant relationship between two elements can be determined by merely examining their codes. While such numbering scheme can greatly improve query performance, renumbering large amount of elements caused by updates becomes a performance bottleneck if XML documents are frequently updated. Unfortunately, no satisfactory work has been reported for efficient update of XML data. In this paper, we first formalize the XML data update problem by defining the basic operators to support most XML update queries. We then present a new numbering scheme that not only requires minimal code-length in comparison with existing numbering schema but also improves update performance when XML data is frequently updated at arbitrary positions. The fundamental difference between our new scheme and existing ones is that, instead of maintaining the explicit codes for elements, we only store the necessary information and generate the codes when they are needed in query processing. In addition to present the basic scheme, we also discuss some optimization techniques to further reduce the update cost. Results of a comprehensive performance study are provided to show the advantages of the new scheme.     

Digital narrative conventions in heritage trail mobile apps

ABSTRACT

Galleries, libraries, archives and museums (GLAMs) are increasingly using digital technologies for storytelling and creating mobile applications (apps) for cultural heritage content, but how apps are used in practice to communicate information to users has not been widely studied. A team of people from a heritage organisation, a university, and mobile app development group plan to create a bespoke heritage trail app for Ireland, but to date design conventions/recommendations for this genre are lacking. This article applies a systematic approach to digital narrative content analysis to better understand how apps are being used specifically for heritage trails with the aim of identifying what the common features are, which modalities and narrative techniques are used. The selected corpus included 55 apps downloaded from the Google Play Store. The results of this content analysis—based on the App Walkthrough Method (Light, B., Burgess, J., & Duguay, S. (2018). The walkthrough method: An approach to the study of apps. New Media & Society, 20(3), 881–900)—show that there is a gap between academic research themes/trends and how digital narrative is actually being communicated in the current market, and it aims to inform the future development of heritage trail apps by including a list of design and content features common to this genre.     

Location management techniques for mobile systems

Locating users in mobile environment is an essential problem in PCS that becomes more challenging as the network size increases and the user population grows. In third generation mobile systems, the signaling traffic and processing overhead of location updates is expected to grow tremendously leading to poor performance. Therefore location management schemes should aim at reducing the cost of updates. Yet, the lookup delay should be kept minimum. This paper aims at classifying the various approaches used for location management of mobile users by grouping them into two main categories. The first category comprises techniques that focused on reducing the cost of looking up a user and adjusted the update process accordingly. These are classified into replication, caching and selective paging techniques. The second category consists of techniques that focused on reducing the cost of updates and maneuvered the update policy to reduce the lookup cost by informing the system with the maximum possible information about user's mobility. This could be achieved by the use of statistic collection, estimation or prediction processes. We classify those schemes into three main classes: schemes based on forwarding pointers, learning-based schemes and prediction-based schemes. By investigating the technical significance of each class a new direction for future research is proposed which favors the second category of location techniques and emphasis the importance of adopting suitable learning and prediction techniques to optimize the overall location cost.     

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.     

Measuring the Usability of Mobile Applications for Phones and Tablets

The use of applications on mobile devices has reached historic levels. Using the System Usability Scale (SUS), data were collected on the usability of applications used on two kinds of mobile platforms—phones and tablets—across two general classes of operating systems, iOS and Android. Over 4 experiments, 3,575 users rated the usability of 10 applications that had been selected based on their popularity, as well as 5 additional applications that users had identified as using frequently. The average SUS rating for the top 10 apps across all platforms was 77.7, with a nearly 20-point spread (67.7–87.4) between the highest and lowest rated apps. Overall, applications on phone platforms were judged to be more usable than applications on the tablet platforms. Practitioners can use the information in this article to make better design decisions and benchmark their progress against a known universe of apps for their specific mobile platform.     

Effects of the aesthetic design of icons on app downloads: evidence from an android market

With the rapid development of the mobile app market, understanding the determinants of mobile app success has become vital to researchers and mobile app developers. Extant research on mobile applications primarily focused on the numerical and textual attributes of apps. Minimal attention has been provided to how the visual attributes of apps affect the download behavior of users. Among the features of app “appearance”, this study focuses on the effects of app icon on demand. With aesthetic product and interface design theories, we analyze icons from three aspects, namely, color, complexity, and symmetry, through image processing. Using a dataset collected from one of the largest Chinese Android websites, we find that icon appearance influences the download behavior of users. Particularly, apps with icons featuring higher colorfulness, proper complexity, and slight asymmetry lead to more downloads. These findings can help developers design their apps.