多维应用特征融合的用户偏好预测

近年来,随着智能手机的飞速发展,移动应用的数目也快速增长。因此,移动应用开发者会提前预测用户对于自己开发的应用的偏好情况。选取Android应用的被卸载次数与其被下载次数的比值作为用户偏好的隐式反映,用户对应用的评价(喜爱率)作为用户偏好的显式反映。基于国内某知名手机应用市场提供的2014年5月至9月的大规模真实用户使用数据,选取9 795个活跃用户数不少于50的Android手机应用作为研究对象,进行分析。从7个维度定义了可能影响用户对应用偏好的30种特征,并对每个应用进行特征提取。基于定义的特征,使用随机森林算法训练分类器,按照卸载/下载比率或喜爱率的高低对应用进行划分,并找出显著影响卸载/下载比率、喜爱率的特征。     

Borland发布JBuilder产品蓝图及新一代基于Eclipse协作式开发环境

Borland下一代Java开发解决方案将深化开发者在软件交付生命周期中的角色全球领先的软件交付优化(SDO)解决方案厂商——美国Borland软件公司(纳斯达克上市编号:BORL)揭示其屡获殊荣的Java集成开发环境JBuilder(?)未来发展的蓝图,细节包括加大以Eclipse作为JBuilder集成框架的投资和引入崭新的开发者协柞和生产力功能,并清晰指出开发者可如何尽享Bodand软件交付优化(SDO)理念的优势。     

跨平台技术在矿山监测系统中的研究与应用

由于移动软件开发平台分类多样,而且个个平台之间互不兼容,致使开发者需要花费大量的时间在软件的修改移植和维护方面.文章融合了Native App与Web App开发模式的优点,采用混合开发模型（Hybrid App）,以及与HTML5提供的Web Storage功能、跨平台等特性结合,提出了一种跨平台（一次编码,多处部署）的应用开发方案.将这种开发方案应用于矿山监测系统的开发,并对用户登录密码通过加密算法进行加密,保证用户数据信息的安全.矿山管理人员能够随时、随地的通过这款app监测矿山内部运作环境及井下人员分布情况,实现了实时有效地监控,减少矿山事故的发生.     

基于前缀投影技术的大规模轨迹预测模型

智能手机、车载GPS终端、可穿戴设备产生了海量的轨迹数据,这些数据不仅描述了移动对象的历史轨迹,而且精确地反映出移动对象的运动特点.已有轨迹预测方法的不足在于：不能同时兼具预测的准确性和时效性,有效的轨迹预测受限于路网等局部空间范围,无法处理复杂、大规模位置数据.为了解决上述问题,针对海量移动对象轨迹数据,结合频繁序列模式发现的思想,提出了基于前缀投影技术的轨迹预测模型PPTP（prefix projection based trajectory prediction model）,包含两个关键步骤：（1）挖掘频繁轨迹模式,构造投影数据库并递归挖掘频繁前序轨迹模式;（2）轨迹匹配,以不同频繁序列模式作为前缀增量式扩展生成频繁后序轨迹,将大于最小支持度阈值的最长连续轨迹作为结果输出.算法的优势在于：可以通过较短的频繁序列模式,增量式生成长轨迹模式;不会产生无用的候选轨迹,弥补频繁模式挖掘计算代价较高的不足.利用真实大规模轨迹数据进行多角度实验,表明PPTP轨迹预测算法具有较高的预测准确性,相对于1阶马尔可夫链预测算法,其平均预测准确率可以提升39.8%.基于所提出的轨迹预测模型,开发了一个通用的轨迹预测系统,能够可视化输出完整的轨迹路线,为用户路径规划提供辅助决策支持.     

移动社交网的生命周期评估模型研究

类似于自然界中的其它系统,移动社交网(MSN)也会存在一个从生到死的“生命周期”.文中着重研究MSN的生命周期及其评估模型,并探究处于不同生命周期阶段的MSN用户行为特征.提出了一个基于波士顿矩阵的移动社交网生命周期模型.该模型通过分析用户点击流数据,判断某个移动社交网站所处的生命周期阶段:幼年期、成长期、成熟期或衰老期.模型从移动互联网的用户访问行为中提取出两个参数指标,分别表征竞争地位与竞争实力,避免了以往需要通过长期市场调研的方法来判断分析对象所处生命周期阶段的困难,可以快速有效地判断一个移动社交网所处的生命周期阶段.根据实地采集的某移动互联网运营商的数据集,分析和验证了文中模型的有效性.此外,从6个方面分析了移动社交网用户在不同生命周期阶段的行为特征.     

Android权限滥用检测系统

凭借开源策略及精准的市场定位,Android系统占据了智能移动终端操作系统84.2%的市场份额.然而,其开放的权限机制带来更多使用者和开发者的同时,也带来了相应的安全问题.中国互联网络信息中心调查数据显示,仅有44.4%的用户在下载安装Android应用的过程中会仔细查看授权说明,而大部分人存在着盲目授权的行为.对于应用开发者来说,由于缺乏安全开发监管,缺乏权限申请相关代码规范,权限滥用问题在Android应用开发中普遍存在,严重影响了代码的规范和质量.其次,用户的盲目授权和软件开发者的权限申请滥用也是用户信息泄露的主要原因,存在严重的安全风险.针对以上问题,本文在现有的权限检测方案基础上,设计和实现了一套新的权限滥用检测系统PACS（Pemission Abuse Checking System）.PACS针对1077个应用进行分析,发现812个应用存在权限滥用问题,约占全部应用的75.4%,同时对实验结果进行抽样验证,证明了PACS的权限检测结果的准确性和有效性.     

晶合热点

CocoaChina开发者大会在京召开 2013年（秋季）CocoaChil3a开发者大会于9月27日在北京国家会议中心召开。大会主题为”Together We Create”（我们一起创造）,旨在联合移动互联网行业所有开发者．一起创建全球开发者的健康生态圈。与会嘉宾通过现场分享开发经验、提供权威数据、预测发展趋势等方式深入探讨移动游戏跨平台的产品与市场。包括触控科技CEO陈吴芝在内的多名业内人士发表了主题演讲。大会现场还举办了“星计划”-手游创新大赛颁奖典礼。     

面向位置预测的动态轨迹模式挖掘

针对海量的用户轨迹数据进行研究,提出一种动态分析移动对象轨迹模式、预测轨迹位置的方法（PRED）。首先使用改进的模式挖掘模型,提取轨迹频繁模式（简称T-模式）,然后提出DPTUpdate算法,设计蕴含时空信息的快捷数据结构--DPT（Dynamic Pattern Tree）,存储和查询移动物体的T-模式,并提出Prediction算法计算最佳匹配度,得到移动对象轨迹的预测位置。PRED方法可提供动态分析的能力,基于真实数据集进行对比实验,结果证明,平均准确率达到72%,平均覆盖率达到92.1%,与已有方法相比,其预测效果有显著提升。     

一种基于MUI的跨平台移动应用开发方案

随着移动互联时代的高速发展,越来越多的开发者致力于用移动开发技术开发出低成本、高品质的APP.基于HTML5的跨平台移动开发技术,给出一种利用MUI框架、Ajax技术、5+Runtime就可方便快速地开发跨平台移动应用的方案.既改善了HTML5开发Web APP无法调用平台资源的问题,又比使用原生开发技术周期短,使开发者在不进行原生APP开发的情况下,让用户达到APP的体验效果最接近原生.     

GE公司推出工业互联网开发者软件云平台

2015年9月29日,通用电气（GE）公司公布工业互联网系列新产品和新的合作伙伴。GE的软件及其相关解决方案业务2015年的收入已超过50亿美元,订单额达到60亿美元。GE宣布的创新之一是GE为工业开发者推出的工业云平台--Predix.io。GE Predix和开发者门户,以及一些相关应用将于2015年底上线,为GE及客户加快上市速度,实现本地开发、全球交付,并加快实现价值。     

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。     

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.     

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.     

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.     

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.     

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.     

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.

     

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.     

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.