What people complain about drone apps? a large-scale empirical study of Google play store reviews

Within the past few years, there has been a tremendous increase in the number of UAVs (Unmanned Aerial Vehicle) or drones manufacture and purchase. It is expected to proliferate further, penetrating into every stream of life, thus making its usage inevitable. The UAV’s major components are its physical hardware and programming software, which controls its navigation or performs various tasks based on the field of concern. The drone manufacturers launch the controlling app for the drones in mobile app stores. A few drone manufacturers also release development kits to aid drone enthusiasts in developing customized or more creative apps. Thus, the app stores are also expected to be flooded with drone-related apps in the near future. With various active research and studies being carried out in UAV’s hardware field, no effort is dedicated to studying/researching the software side of UAV. Towards this end, a large-scale empirical study of UAV or drone-related apps of the Google Play Store Platform is conducted. The study consisted of 1,825 UAV mobile apps, across twenty-five categories, with 162,250 reviews. Some of the notable findings of the thesis are (a) There are 27 major types of issues the drone app users complain about, (b) The top four complaints observed are Functional Error (27.9%), Device Compatibility (16.8%), Cost (16.2%) and Connection/Sync (15.6%), (c) The top four issues for which the UAV manufactures or Drone app developers provide feedback to user complaints are Functional Error (40.9%), Cost (33.3%), Device Compatibility (23.1%) and ConnectionSync (16%), (d) Developers respond to the most frequently occurring complaints rather than the most negatively impacting ones

Detection of spam review on mobile app stores, evaluation of helpfulness of user reviews and extraction of quality aspects using machine learning techniques

As mobile devices have overtaken fixed Internet access, mobile applications and distribution platforms have gained in importance. App stores enable users to search and purchase mobile applications and then to give feedback in the form of reviews and ratings. A review might contain critical information about user experience, feature requests and bug reports. User reviews are valuable not only to developers and software organizations interested in learning the opinion of their customers but also to prospective users who would like to find out what others think about an app. Even though some surveys have inventoried techniques and methods in opinion mining and sentiment analysis, no systematic literature review (SLR) study had yet reported on mobile app store opinion mining and spam review detection problems. Mining opinions from app store reviews requires pre-processing at the text and content levels, including filtering-out nonopinionated content and evaluating trustworthiness and genuineness of the reviews. In addition, the relevance of the extracted features are not cross-validated with main software engineering concepts. This research project first conducted a systematic literature review (SLR) on the evaluation of mobile app store opinion mining studies. Next, to fill the identified gaps in the literature, we used a novel convolutional neural network to learn document representation for deceptive spam review detection by characterizing an app store review dataset which includes truthful and spam reviews for the first time in the literature. Our experiments reported that our neural network based method achieved 82.5% accuracy, while a baseline Support Vector Machine (SVM) classification model reached only 70% accuracy despite leveraging various feature combinations. We next compared four classification models to assess app store user review helpfulness and proposed a predictive model which makes use of review meta-data along with structural and lexical features for helpfulness prediction. In the last part of this research study, we constructed an annotated app store review dataset for the aspect extraction task, based on ISO 25010 - Systems and software Product Quality Requirements and Evaluation standard and two deep neural network models: Bi-directional Long-Short Term Memory and Conditional Random Field (Bi-LSTM+CRF) and Deep Convolutional Neural Networks and Conditional Random Field (CNN+CRF) for aspect extraction from app store user reviews. Both models achieved nearly 80% F1 score (the weighted average of precision and recall which takes both false positives and false negatives into account) in exact aspect matching and 86% F1 score in partial aspect matching

Letters from the War of Ecosystems – An Analysis of Independent Software Vendors in Mobile Application Marketplaces

The recent emergence of a new generation of mobile application marketplaces has changed the business in the mobile ecosystems. The marketplaces have gathered over a million applications by hundreds of thousands of application developers and publishers. Thus, software ecosystems—consisting of developers, consumers and the orchestrator—have emerged as a part of the mobile ecosystem. This dissertation addresses the new challenges faced by mobile application developers in the new ecosystems through empirical methods. By using the theories of two-sided markets and business ecosystems as the basis, the thesis assesses monetization and value creation in the market as well as the impact of electronic Word-of-Mouth (eWOM) and developer multihoming— i. e. contributing for more than one platform—in the ecosystems. The data for the study was collected with web crawling from the three biggest marketplaces: Apple App Store, Google Play and Windows Phone Store. The dissertation consists of six individual articles. The results of the studies show a gap in monetization among the studied applications, while a majority of applications are produced by small or micro-enterprises. The study finds only weak support for the impact of eWOM on the sales of an application in the studied ecosystem. Finally, the study reveals a clear difference in the multi-homing rates between the top application developers and the rest. This has, as discussed in the thesis, an impact on the future market analyses—it seems that the smart device market can sustain several parallel application marketplaces.Muutama vuosi sitten julkistetut uuden sukupolven mobiilisovellusten kauppapaikat ovat muuttaneet mobiiliekosysteemien liiketoimintadynamiikkaa. Nämä uudet markkinapaikat ovat jo onnistuneet houkuttelemaan yli miljoona sovellusta sadoilta tuhansilta ohjelmistokehittäjiltä. Nämä kehittäjät yhdessä markkinapaikan organisoijan sekä loppukäyttäjien kanssa ovat muodostaneet ohjelmistoekosysteemin osaksi laajempaa mobiiliekosysteemiä. Tässä väitöskirjassa tarkastellaan mobiilisovellusten kehittäjien uudenlaisilla kauppapaikoilla kohtaamia haasteita empiiristen tutkimusmenetelmien kautta. Väitöskirjassa arvioidaan sovellusten monetisaatiota ja arvonluontia sekä verkon asiakasarviointien (engl. electronicWord-of-Mouth, eWOM) ja kehittäjien moniliittymisen (engl. multi-homing) — kehittäjä on sitoutunut useammalle kuin yhdelle ekosysteemille — vaikutuksia ekosysteemissä. Työn teoreettinen tausta rakentuu kaksipuolisten markkinapaikkojen ja liiketoimintaekosysteemien päälle. Tutkimuksen aineisto on kerätty kolmelta suurimmalta mobiilisovellusmarkkinapaikalta: Apple App Storesta, Google Playstä ja Windows Phone Storesta. Tämä artikkeliväitöskirja koostuu kuudesta itsenäisestä tutkimuskäsikirjoituksesta. Artikkelien tulokset osoittavat puutteita monetisaatiossa tutkittujen sovellusten joukossa. Merkittävä osa tarkastelluista sovelluksista on pienten yritysten tai yksittäisten kehittäjien julkaisemia. Tutkimuksessa löydettiin vain heikkoa tukea eWOM:in positiiviselle vaikutukselle sovellusten myyntimäärissä. Työssä myös osoitetaan merkittävä ero menestyneimpien sovelluskehittäjien sekä muiden kehittäjien moniliittymiskäyttäytymisen välillä. Tällä havainnolla on merkitystä tuleville markkina-analyyseille ja sen vaikutuksia on käsitelty työssä. Tulokset esimerkiksi viittaavat siihen, että markkinat pystyisivät ylläpitämään useita kilpailevia kauppapaikkoja.Siirretty Doriast

Software Engineering in the Age of App Stores: Feature-Based Analyses to Guide Mobile Software Engineers

Mobile app stores are becoming the dominating distribution platform of mobile applications. Due to their rapid growth, their impact on software engineering practices is not yet well understood. There has been no comprehensive study that explores the mobile app store ecosystem's effect on software engineering practices. Therefore, this thesis, as its first contribution, empirically studies the app store as a phenomenon from the developers' perspective to investigate the extent to which app stores affect software engineering tasks. The study highlights the importance of a mobile application's features as a deliverable unit from developers to users. The study uncovers the involvement of app stores in eliciting requirements, perfective maintenance and domain analysis in the form of discoverable features written in text form in descriptions and user reviews. Developers discover possible features to include by searching the app store. Developers, through interviews, revealed the cost of such tasks given a highly prolific user base, which major app stores exhibit. Therefore, the thesis, in its second contribution, uses techniques to extract features from unstructured natural language artefacts. This is motivated by the indication that developers monitor similar applications, in terms of provided features, to understand user expectations in a certain application domain. This thesis then devises a semantic-aware technique of mobile application representation using textual functionality descriptions. This representation is then shown to successfully cluster mobile applications to uncover a finer-grained and functionality-based grouping of mobile apps. The thesis, furthermore, provides a comparison of baseline techniques of feature extraction from textual artefacts based on three main criteria: silhouette width measure, human judgement and execution time. Finally, this thesis, in its final contribution shows that features do indeed migrate in the app store beyond category boundaries and discovers a set of migratory characteristics and their relationship to price, rating and popularity in the app stores studied

iPerfDetector: Characterizing and Detecting Performance Anti-patterns in iOS Applications

Performance issues in mobile applications (i.e., apps) often have a direct impact on the user experience. However, due to limited testing resources and fast-paced software development cycles, many performance issues remain undiscovered when the apps are released. As found by a prior study, these performance issues are one of the most common complaints that app users have. Unfortunately, there is a limited support to help developers avoid or detect performance issues in mobile apps. In this thesis, we conduct an empirical study on performance issues in iOS apps written in Swift language. To the best of our knowledge, this is the first study on performance issues of apps on the iOS platform. We manually studied 235 performance issues that are collected from four open source iOS apps. We found that most performance issues in iOS apps are related to inefficient UI design, memory issues, and inefficient thread handling. We also manually uncovered four performance anti-patterns that recurred in the studied issue reports. To help developers avoid these performance anti-patterns in the code, we implemented a static analysis tool called iPerfDetector. We evaluated iPerfDetector on eight open source and three commercial apps. iPerfDetector successfully detected 34 performance anti-pattern instances in the studied apps, where 31 of them are already confirmed and accepted by developers as potential performance issues. Our case study on the performance impact of the anti-patterns shows that fixing the anti-pattern may improve the performance (i.e., response time, GPU, or CPU) of the workload by up to 80%

App Store Analysis for Software Engineering

App Store Analysis concerns the mining of data from apps, made possible through app stores. This thesis extracts publicly available data from app stores, in order to detect and analyse relationships between technical attributes, such as software features, and non-technical attributes, such as rating and popularity information. The thesis identifies the App Sampling Problem, its effects and a methodology to ameliorate the problem. The App Sampling Problem is a fundamental sampling issue concerned with mining app stores, caused by the rather limited ‘most-popular-only’ ranked app discovery present in mobile app stores. This thesis provides novel techniques for the analysis of technical and non-technical data from app stores. Topic modelling is used as a feature extraction technique, which is shown to produce the same results as n-gram feature extraction, that also enables linking technical features from app descriptions with those in user reviews. Causal impact analysis is applied to app store performance data, leading to the identification of properties of statistically significant releases, and developer-controlled properties which could increase a release’s chance for causal significance. This thesis introduces the Causal Impact Release Analysis tool, CIRA, for performing causal impact analysis on app store data, which makes the aforementioned research possible; combined with the earlier feature extraction technique, this enables the identification of the claimed software features that may have led to significant positive and negative changes after a release

Mining app reviews to support software engineering

The thesis studies how mining app reviews can support software engineering. App reviews —short user reviews of an app in app stores— provide a potentially rich source of information to help software development teams maintain and evolve their products. Exploiting this information is however difficult due to the large number of reviews and the difficulty in extracting useful actionable information from short informal texts. A variety of app review mining techniques have been proposed to classify reviews and to extract information such as feature requests, bug descriptions, and user sentiments but the usefulness of these techniques in practice is still unknown. Research in this area has grown rapidly, resulting in a large number of scientific publications (at least 182 between 2010 and 2020) but nearly no independent evaluation and description of how diverse techniques fit together to support specific software engineering tasks have been performed so far. The thesis presents a series of contributions to address these limitations. We first report the findings of a systematic literature review in app review mining exposing the breadth and limitations of research in this area. Using findings from the literature review, we then present a reference model that relates features of app review mining tools to specific software engineering tasks supporting requirements engineering, software maintenance and evolution. We then present two additional contributions extending previous evaluations of app review mining techniques. We present a novel independent evaluation of opinion mining techniques using an annotated dataset created for our experiment. Our evaluation finds lower effectiveness than initially reported by the techniques authors. A final part of the thesis, evaluates approaches in searching for app reviews pertinent to a particular feature. The findings show a general purpose search technique is more effective than the state-of-the-art purpose-built app review mining techniques; and suggest their usefulness for requirements elicitation. Overall, the thesis contributes to improving the empirical evaluation of app review mining techniques and their application in software engineering practice. Researchers and developers of future app mining tools will benefit from the novel reference model, detailed experiments designs, and publicly available datasets presented in the thesis

Improving Application Quality using Mobile Analytics

The purpose of this research is to investigate and report on how mobile analytics can help real-world developers improve the quality of their apps efficiently and effectively. The research also considers the effects of mobile analytics in terms of the artefacts developed and maintained by the development team and also researches key characteristics of a range of mobile analytics tools and services. Research Design: the research takes a developer-oriented perspective of using three complementary sources of data: 1) platform-level analytics, using Android Vitals as the primary analytics tool, 2) in-app analytics with a focus on runtime failures caused by crashes and freezes (known as Application Not Responding (ANR) in Android), and 3) interviews with developers. Action research techniques included roles of embedded developer, guide, and observer across different mobile app projects I was involved in. Hackathons were used to experiment with the speed and ability to find and address issues reported by the analytics tools used by the app developers. Their apps have a combined active user base of over 3,000,000 users. Many of these apps use a mainstream crash analytics library which was used to complement and contrast the results provided in the primary analytics tool. The research is intended to facilitate ease of future research and reproducibility, e.g. by using open-source projects as the code, bug reports, etc. are all published and available. This research was complemented by a) collaborating with professional developers who provided additional examples and results, and b) investigating grey material including grey literature and grey data. The findings of this research highlights that using mobile analytics helped to reduce failure rates markedly, quickly, and effectively by applying techniques described here. Various limitations and flaws were found in the analytics tools; these provide cause for concern as they may affect the app’s placement in the app store and revenues. These limitations and flaws also make some issues in the apps harder to identify, prioritise, and fix.We identified ways to compensate for many of these and developed open-source software to facilitate additional analysis. Flaws and bugs were reported to the Android Vitals team at Google who acknowledged they would fix several of them. Several bugs were hard to reproduce, partly as Google deliberately hid pertinent details from the data they gather. Nonetheless app developers were able to ameliorate or fix the bugs for some issues even when they were not able to reproduce them. Android Vitals shows the potential of how the combination of an app store and platform could be used to improve the quality of apps without users needing to actively participate. Some crashes were hard to reproduce and may be impractical to find before the app is released to end users. Developers can determine comparative improvements in their releases, such as whether they fixed a bug, by using Android Vitals and similar analytics tools; i.e. mobile analytics may help teams to determine whether they have improved the quality of their app even with flaws and limitations in the mobile analytics.</i

Exploring Language Learning with Mobile Technology: A Qualitative Content Analysis of Vocabulary Learning Apps for ESL Learners in Canada

Learning apps are becoming ubiquitous in and out of the classroom. While the number of ESL learnings apps has been increasing dramatically, not much information is available for teachers and learners to evaluate the quality of these apps. The purpose of this study was to explore the apps that are most commonly recommended for language learning, investigate features of commonly recommended ESL learning apps, and develop an app evaluation tool that might inform selection of ESL learning apps for use in teaching or recommendations to parents and learners. This study used qualitative content analysis to study selected vocabulary learning apps. Findings show that there is a lack of ESL reading and writing apps, and the selected apps do not have all the exemplar app features in curriculum, pedagogy, and design. The author developed an app evaluation checklist based on the existing literature, Ontario ESL curriculum, and on the emergent app features in the findings. The findings of this study have potential to guide administrators, policy makers, educators, teachers, and individual learners when selecting quality, productive, and well-designed apps