The Experts below are selected from a list of 4296 Experts worldwide ranked by ideXlab platform
Guillaume Jourjon - One of the best experts on this subject based on the ideXlab platform.
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a multi modal neural embeddings approach for detecting mobile counterfeit apps a case study on Google Play Store
IEEE Transactions on Mobile Computing, 2020Co-Authors: Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume JourjonAbstract:Counterfeit apps impersonate existing popular apps to misguide users to install them for various reasons such as collecting information, spreading malware, or increasing advertisement revenue. Many counterfeits can be identified once installed, however even users may struggle to detect them before installation as app icons and descriptions can be quite similar to the original app. This paper leverages recent advances in deep learning to efficiently identify counterfeits. We show that for the problem of counterfeit detection, a novel approach of combining content embeddings and style embeddings outperforms the baseline methods. We first evaluate the performance of the proposed method on two standard datasets and show that content, style, and combined embeddings increase precision@k and recall@k by 10%-15% and 12%-25%, respectively. Second, for the app counterfeit detection problem, we show that combined content and style embeddings achieve better performance. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits. Under a conservative assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps.
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a multi modal neural embeddings approach for detecting mobile counterfeit apps a case study on Google Play Store
arXiv: Cryptography and Security, 2020Co-Authors: Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume JourjonAbstract:Counterfeit apps impersonate existing popular apps in attempts to misguide users to install them for various reasons such as collecting personal information or spreading malware. Many counterfeits can be identified once installed, however even a tech-savvy user may struggle to detect them before installation. To this end, this paper proposes to leverage the recent advances in deep learning methods to create image and text embeddings so that counterfeit apps can be efficiently identified when they are submitted for publication. We show that a novel approach of combining content embeddings and style embeddings outperforms the baseline methods for image similarity such as SIFT, SURF, and various image hashing methods. We first evaluate the performance of the proposed method on two well-known datasets for evaluating image similarity methods and show that content, style, and combined embeddings increase precision@k and recall@k by 10%-15% and 12%-25%, respectively when retrieving five nearest neighbours. Second, specifically for the app counterfeit detection problem, combined content and style embeddings achieve 12% and 14% increase in precision@k and recall@k, respectively compared to the baseline methods. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits for top-10,000 popular apps. Under a conservative assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps in Google Play Store. We also find 1,565 potential counterfeits asking for at least five additional dangerous permissions than the original app and 1,407 potential counterfeits having at least five extra third party advertisement libraries.
Ahmed E. Hassan - One of the best experts on this subject based on the ideXlab platform.
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studying bad updates of top free to download apps in the Google Play Store
IEEE Transactions on Software Engineering, 2020Co-Authors: Safwat Hassan, Cor-paul Bezemer, Ahmed E. HassanAbstract:Developers always focus on delivering high-quality updates to improve, or maintain the rating of their apps. Prior work has studied user reviews by analyzing all reviews of an app. However, this app-level analysis misses the point that users post reviews to provide their feedback on a certain update. For example, two bad updates of an app with a history of good updates would not be spotted using app-level analysis. In this paper, we examine reviews at the update-level to better understand how users perceive bad updates. We focus our study on the top 250 bad updates (i.e., updates with the highest increase in the percentage of negative reviews relative to the prior updates of the app) from 26,726 updates of 2,526 top free-to-download apps in the Google Play Store. We find that feature removal and UI issues have the highest increase in the percentage of negative reviews. Bad updates with crashes and functional issues are the most likely to be fixed by a later update. However, developers often do not mention these fixes in the release notes. Our work demonstrates the necessity of an update-level analysis of reviews to capture the feelings of an app's user-base about a particular update.
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A longitudinal study of popular ad libraries in the Google Play Store
Empirical Software Engineering, 2019Co-Authors: Md Ahasanuzzaman, Safwat Hassan, Cor-paul Bezemer, Ahmed E. HassanAbstract:In-app advertisements have become an integral part of the revenue model of mobile apps. To gain ad revenue, app developers integrate ad libraries into their apps. Such libraries are integrated to serve advertisements (ads) to users; developers then gain revenue based on the disPlayed ads and the users’ interactions with such ads. As a result, ad libraries have become an essential part of the mobile app ecosystem. However, little is known about how such ad libraries have evolved over time. In this paper, we study the evolution of the 8 most popular ad libraries (e.g., Google AdMob and Facebook Audience Network) over a period of 33 months (from April 2016 until December 2018). In particular, we look at their evolution in terms of size, the main drivers for releasing a new version, and their architecture. To identify popular ad libraries, we collect 35,462 updates of 1,840 top free-to-download apps in the Google Play Store. Then, we identify 63 ad libraries that are integrated into the studied popular apps. We observe that an ad library represents 10% of the binary size of mobile apps, and that the proportion of the ad library size compared to the app size has grown by 10% over our study period. By taking a closer look at the 8 most popular ad libraries, we find that ad libraries are continuously evolving with a median release interval of 34 days. In addition, we observe that some libraries have grown exponentially in size (e.g, Facebook Audience Network), while other libraries have attempted to reduce their size as they evolved. The libraries that reduced their size have done so through: (1) creating a lighter version of the ad library, (2) removing parts of the ad library, and (3) redesigning their architecture into a more modular one. To identify the main drivers for releasing a new version, we manually analyze the release notes of the eight studied ad libraries. We observe that fixing issues that are related to disPlaying video ads is the main driver for releasing new versions. We also observe that ad library developers are constantly updating their libraries to support a wider range of Android platforms (i.e., to ensure that more devices can use the libraries without errors). Finally, we derive a reference architecture from the studied eight ad libraries, and we study how these libraries deviated from this architecture in the study period. Our study is important for ad library developers as it provides the first in-depth look into how the important mobile app market segment of ad libraries has evolved. Our findings and the reference architecture are valuable for ad library developers who wish to learn about how other developers built and evolved their successful ad libraries. For example, our reference architecture provides a new ad library developer with a foundation for understanding the interactions between the most important components of an ad library.
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Studying the dialogue between users and developers of free apps in the Google Play Store
Empirical Software Engineering, 2018Co-Authors: Safwat Hassan, Cor-paul Bezemer, Chakkrit Tantithamthavorn, Ahmed E. HassanAbstract:The popularity of mobile apps continues to grow over the past few years. Mobile app Stores, such as the Google Play Store and Apple’s App Store provide a unique user feedback mechanism to app developers through the possibility of posting app reviews. In the Google Play Store (and soon in the Apple App Store), developers are able to respond to such user feedback. Over the past years, mobile app reviews have been studied excessively by researchers. However, much of prior work (including our own prior work) incorrectly assumes that reviews are static in nature and that users never update their reviews. In a recent study, we started analyzing the dynamic nature of the review-response mechanism. Our previous study showed that responding to a review often has a positive effect on the rating that is given by the user to an app. In this paper, we revisit our prior finding in more depth by studying 4.5 million reviews with 126,686 responses for 2,328 top free-to-download apps in the Google Play Store. One of the major findings of our paper is that the assumption that reviews are static is incorrect. In particular, we find that developers and users in some cases use this response mechanism as a rudimentary user support tool, where dialogues emerge between users and developers through updated reviews and responses. Even though the messages are often simple, we find instances of as many as ten user-developer back-and-forth messages that occur via the response mechanism. Using a mixed-effect model, we identify that the likelihood of a developer responding to a review increases as the review rating gets lower or as the review content gets longer. In addition, we identify four patterns of developers: 1) developers who primarily respond to only negative reviews, 2) developers who primarily respond to negative reviews or to reviews based on their contents, 3) developers who primarily respond to reviews which are posted shortly after the latest release of their app, and 4) developers who primarily respond to reviews which are posted long after the latest release of their app. We perform a qualitative analysis of developer responses to understand what drives developers to respond to a review. We manually analyzed a statistically representative random sample of 347 reviews with responses for the top ten apps with the highest number of developer responses. We identify seven drivers that make a developer respond to a review, of which the most important ones are to thank the users for using the app and to ask the user for more details about the reported issue. Our findings show that it can be worthwhile for app owners to respond to reviews, as responding may lead to an increase in the given rating. In addition, our findings show that studying the dialogue between user and developer can provide valuable insights that can lead to improvements in the app Store and user support process.
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studying the dialogue between users and developers of free apps in the Google Play Store
International Conference on Software Engineering, 2018Co-Authors: Safwat Hassan, Cor-paul Bezemer, Chakkrit Tantithamthavorn, Ahmed E. HassanAbstract:The popularity of mobile apps continues to grow over the past few years. Mobile app Stores, such as the Google Play Store and Apple's App Store provide a unique user feedback mechanism to app developers through app reviews. In the Google Play Store (and most recently in the Apple App Store), developers are able to respond to such user feedback. Over the past years, mobile app reviews have been studied excessively by researchers. However, much of prior work (including our own prior work) incorrectly assumes that reviews are static in nature and that users never update their reviews. In a recent study, we started analyzing the dynamic nature of the review-response mechanism. Our previous study showed that responding to a review often has a positive effect on the rating that is given by the user to an app. In this paper [1], we revisit our prior finding in more depth by studying 4.5 million reviews with 126,686 responses of 2,328 top free-to-download apps in the Google Play Store. One of the major findings of our paper is that the assumption that reviews are static is incorrect. In particular, we find that developers and users in some cases use this response mechanism as a rudimentary user support tool, where dialogues emerge between users and developers through updated reviews and responses. Even though the messages are often simple, we find instances of as many as ten user-developer back-and-forth messages that occur via the response mechanism. Using a mixed-effect model, we identify that the likelihood of a developer responding to a review increases as the review rating gets lower or as the review content gets longer. In addition, we identify four patterns of developers: 1) developers who primarily respond to only negative reviews, 2) developers who primarily respond to negative reviews or to reviews based on their content, 3) developers who primarily respond to reviews which are posted shortly after the latest release of their app, and 4) developers who primarily respond to reviews which are posted long after the latest release of their app. We perform a qualitative analysis of developer responses to understand what drives developers to respond to a review. We manually analyzed a statistically representative random sample of 347 reviews with responses of the top ten apps with the highest number of developer responses. We identify seven drivers that make a developer respond to a review, of which the most important ones are to thank the users for using the app and to ask the user for more details about the reported issue. Our findings show that it can be worthwhile for app owners to respond to reviews, as responding may lead to an increase in the given rating. In addition, our findings show that studying the dialogue between users and developers provides valuable insights that can lead to improvements in the app Store and the user support process. The main contributions of this paper are as follows: (1) Our paper is the first work to demonstrate the dynamic nature of reviews. (2) Furthermore, we are the first to demonstrate a peculiar use of the app-review platforms as a user support medium. (3) In addition, our work is the first work to deeply explore developer responses in a systematic manner. (4) Finally, our classification of developer-responses highlights the value of providing canned or even automated responses in next generation app-review platforms.
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journal first studying the dialogue between users and developers of free apps in the Google Play Store
International Conference on Software Engineering, 2018Co-Authors: Safwat Hassan, Cor-paul Bezemer, Chakkrit Tantithamthavorn, Ahmed E. HassanAbstract:The popularity of mobile apps continues to grow over the past few years. Mobile app Stores, such as the Google Play Store and Apple's App Store provide a unique user feedback mechanism to app developers through app reviews. In the Google Play Store (and most recently in the Apple App Store), developers are able to respond to such user feedback. Over the past years, mobile app reviews have been studied excessively by researchers. However, much of prior work (including our own prior work) incorrectly assumes that reviews are static in nature and that users never update their reviews. In a recent study, we started analyzing the dynamic nature of the review-response mechanism. Our previous study showed that responding to a review often has a positive effect on the rating that is given by the user to an app. In this paper [1], we revisit our prior finding in more depth by studying 4.5 million reviews with 126,686 responses of 2,328 top free-to-download apps in the Google Play Store. One of the major findings of our paper is that the assumption that reviews are static is incorrect. In particular, we find that developers and users in some cases use this response mechanism as a rudimentary user support tool, where dialogues emerge between users and developers through updated reviews and responses. Even though the messages are often simple, we find instances of as many as ten user-developer back-and-forth messages that occur via the response mechanism. Using a mixed-effect model, we identify that the likelihood of a developer responding to a review increases as the review rating gets lower or as the review content gets longer. In addition, we identify four patterns of developers: 1) developers who primarily respond to only negative reviews, 2) developers who primarily respond to negative reviews or to reviews based on their content, 3) developers who primarily respond to reviews which are posted shortly after the latest release of their app, and 4) developers who primarily respond to reviews which are posted long after the latest release of their app. We perform a qualitative analysis of developer responses to understand what drives developers to respond to a review. We manually analyzed a statistically representative random sample of 347 reviews with responses of the top ten apps with the highest number of developer responses. We identify seven drivers that make a developer respond to a review, of which the most important ones are to thank the users for using the app and to ask the user for more details about the reported issue. Our findings show that it can be worthwhile for app owners to respond to reviews, as responding may lead to an increase in the given rating. In addition, our findings show that studying the dialogue between users and developers provides valuable insights that can lead to improvements in the app Store and the user support process. The main contributions of this paper are as follows: (1) Our paper is the first work to demonstrate the dynamic nature of reviews. (2) Furthermore, we are the first to demonstrate a peculiar use of the app-review platforms as a user support medium. (3) In addition, our work is the first work to deeply explore developer responses in a systematic manner. (4) Finally, our classification of developer-responses highlights the value of providing canned or even automated responses in next generation app-review platforms. The full paper is published in the Empirical So ware Engineering journal, and can be found at: https:// link.springer.com/ article/ 10.1007/ s10664-017-9538-9 Please cite the following paper: Hassan S, Tantithamthavorn C, Bezemer CP, Hassan AE (2017) Studying the dialogue between users and developers of free apps in the Google Play Store. Empirical Software Engineering pp 1–38
Fava, Silvana Maria Coelho Leite - One of the best experts on this subject based on the ideXlab platform.
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Aplicativos móveis sobre hipertensão arterial sistêmica: revisão narrativa / Aplicativos móveis sobre hipertensão arterial sistêmica: revisão narrativa
Brazilian Journals Publicações de Periódicos e Editora Ltda., 2020Co-Authors: Letícia Kühn Da ,silveira, Larissa Oliveira De ,carvalho, Rosa, Letícia Francisco Ferreira, Paraizo, Camila Maria Silva, Dázio, Eliza Maria Rezende, Fava, Silvana Maria Coelho LeiteAbstract:O avanço da tecnologia, associado à alta incidência da Hipertensão Arterial Sistêmica na população mundial influenciaram o desenvolvimento de diversos aplicativos de informação e monitorização para dispositivos móveis relacionados à Hipertensão Arterial. O conhecimento acerca da modalidade e de suas principais funcionalidades é de grande importância para uma melhor atenção a saúde. Nessa perspectiva, esse estudo teve como objetivo conhecer os aplicativos disponíveis no Google Play Store relacionados à Hipertensão Arterial Sistêmica quanto às suas características e às avaliações dos usuários. Trata-se de uma revisão narrativa de literatura. A coleta de dados foi realizada no período de outubro a novembro de 2019 por meio da busca por aplicativos sobre Hipertensão Arterial no Google Play Store, utilizando as palavras “hipertensão” e “hipertensão arterial sistêmica”. Encontrou-se um total de 364 aplicativos, sendo incluídos no estudo 267 distribuídos nas mais diversas categorias, mas com maior concentração: entretenimento; saúde e fitness; e medicina. Constatou-se o desenvolvimento de um grande número de aplicativos relacionados à Hipertensão Arterial Sistêmica, com a função de entretenimento. Pelos comentários e avaliações dos usuários apreende-se que grande parte dos usuários, que os utilizam acreditam que as informações fornecidas por tais aplicativos são verídicas, o que pode colocar em risco a sua saúde. Constatou-se que a crescente inovação tecnológica traz diversos benefícios para a área da saúde, no entanto, é de extrema importância um melhor controle dos benefícios e malefícios dessas tecnologias
Lutfi Budi Ilmawan - One of the best experts on this subject based on the ideXlab platform.
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perbandingan metode klasifikasi support vector machine dan naive bayes untuk analisis sentimen pada ulasan tekstual di Google Play Store
ILKOM Jurnal Ilmiah, 2020Co-Authors: Lutfi Budi Ilmawan, Muhammad Aliyazid MudeAbstract:In this research, the performance of SVM classification method will be compared with other classification methods, by using the Naive Bayes classification method. Naive Bayes classification method is a light classification method and has a high accuracy if applied to the text classification according to some previous studies. The accuracy of the classifier is measured using the K-fold cross validation method whose results will be tabulated in a confusion matrix table, with a value of K = 3. In this study, the data processed are textual reviews of applications in the Indonesian language Google Play Store obtained from previous research. The test results obtained from the 3-fold cross-validation method produce that SVM Classifier has a higher value of accuracy when compared with the accuracy of the Naive Bayes classifier, the SVM classifier gets an accuracy of 81.46% and Naive Bayes classifier by 75.41%.
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membangun web crawler berbasis web service untuk data crawling pada website Google Play Store
ILKOM Jurnal Ilmiah, 2018Co-Authors: Lutfi Budi IlmawanAbstract:At this time, Google Play Store is not providing API that can be used for accessing datas from applications on it’s application Store. With that plenty application’s data, it could be used to make it a good research object, specially on data mining field. In this research, the system that is built is the system that can retrieve that applications’ data. For multiplatform’s purpose, web services are used for being an interface between client and server. Finally, the built system is working as expected. The system can retrive data from Google Play Store and it is suitable from requirements of data analysis stage. It can also integrated with REST web service to provide multiplatform access.
Naveen Karunanayake - One of the best experts on this subject based on the ideXlab platform.
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a multi modal neural embeddings approach for detecting mobile counterfeit apps a case study on Google Play Store
IEEE Transactions on Mobile Computing, 2020Co-Authors: Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume JourjonAbstract:Counterfeit apps impersonate existing popular apps to misguide users to install them for various reasons such as collecting information, spreading malware, or increasing advertisement revenue. Many counterfeits can be identified once installed, however even users may struggle to detect them before installation as app icons and descriptions can be quite similar to the original app. This paper leverages recent advances in deep learning to efficiently identify counterfeits. We show that for the problem of counterfeit detection, a novel approach of combining content embeddings and style embeddings outperforms the baseline methods. We first evaluate the performance of the proposed method on two standard datasets and show that content, style, and combined embeddings increase precision@k and recall@k by 10%-15% and 12%-25%, respectively. Second, for the app counterfeit detection problem, we show that combined content and style embeddings achieve better performance. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits. Under a conservative assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps.
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a multi modal neural embeddings approach for detecting mobile counterfeit apps a case study on Google Play Store
arXiv: Cryptography and Security, 2020Co-Authors: Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume JourjonAbstract:Counterfeit apps impersonate existing popular apps in attempts to misguide users to install them for various reasons such as collecting personal information or spreading malware. Many counterfeits can be identified once installed, however even a tech-savvy user may struggle to detect them before installation. To this end, this paper proposes to leverage the recent advances in deep learning methods to create image and text embeddings so that counterfeit apps can be efficiently identified when they are submitted for publication. We show that a novel approach of combining content embeddings and style embeddings outperforms the baseline methods for image similarity such as SIFT, SURF, and various image hashing methods. We first evaluate the performance of the proposed method on two well-known datasets for evaluating image similarity methods and show that content, style, and combined embeddings increase precision@k and recall@k by 10%-15% and 12%-25%, respectively when retrieving five nearest neighbours. Second, specifically for the app counterfeit detection problem, combined content and style embeddings achieve 12% and 14% increase in precision@k and recall@k, respectively compared to the baseline methods. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits for top-10,000 popular apps. Under a conservative assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps in Google Play Store. We also find 1,565 potential counterfeits asking for at least five additional dangerous permissions than the original app and 1,407 potential counterfeits having at least five extra third party advertisement libraries.
