Pub Date : 2017-05-20DOI: 10.1109/MOBILESoft.2017.13
Carlo Bernaschina, Roman Fedorov, Darian Frajberg, P. Fraternali
Outdoor mobile applications are becoming popular in fields such as gaming, tourism and environment monitoring. They rely on the input of multiple, possibly noisy, sensors, such as the camera, GPS, compass and gyroscope. The regression testing of such applications requires the reproduction of the real conditions in which the application works, which are hard to reproduce without automated support. We present a capture replay framework that automates regression testing of mobile outdoor applications, by recording data streams in real-time on the field from multiple sensors, replays them in lab and computes quality metrics to trace regression errors.
{"title":"A Framework for Regression Testing of Outdoor Mobile Applications","authors":"Carlo Bernaschina, Roman Fedorov, Darian Frajberg, P. Fraternali","doi":"10.1109/MOBILESoft.2017.13","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.13","url":null,"abstract":"Outdoor mobile applications are becoming popular in fields such as gaming, tourism and environment monitoring. They rely on the input of multiple, possibly noisy, sensors, such as the camera, GPS, compass and gyroscope. The regression testing of such applications requires the reproduction of the real conditions in which the application works, which are hard to reproduce without automated support. We present a capture replay framework that automates regression testing of mobile outdoor applications, by recording data streams in real-time on the field from multiple sensors, replays them in lab and computes quality metrics to trace regression errors.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134638560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-20DOI: 10.1109/MOBILESoft.2017.18
Yan Wang, A. Rountev
Android developers commonly use app obfuscation to secure their apps and intellectual property. Although obfuscation provides protection, it presents an obstacle for a number of legitimate program analyses such as detection of app cloning and repackaging, malware detection, identification of third-party libraries, provenance analysis for digital forensics, and reverse engineering for test generation and performance analysis. If the obfuscator used to create an app can be identified, and if some details of the obfuscation process can be inferred, subsequent analyses can exploit this knowledge. Thus, it is desirable to be able to automatically analyze a given app and determine (1) whether it was obfuscated, (2) which obfuscator was used, and (3) how the obfuscator was configured. We have developed novel techniques to identify the obfuscator of an Android app for several widely-used obfuscation tools and for a number of their configuration options. We define the obfuscator identification problem and propose a solution based on machine learning. To the best of our knowledge, this is the first work to formulate and solve this problem. We identify a feature vector that represents the characteristics of the obfuscated code. We then implement a tool that extracts this feature vector from Dalvik bytecode and uses it to identify the obfuscator provenance information. We evaluate the proposed approach on real-world Android apps obfuscated with different obfuscators, under several configurations. Our experiments indicate that the approach identifies the obfuscator with about 97% accuracy and recognizes the configuration with more than 90% accuracy.
{"title":"Who Changed You? Obfuscator Identification for Android","authors":"Yan Wang, A. Rountev","doi":"10.1109/MOBILESoft.2017.18","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.18","url":null,"abstract":"Android developers commonly use app obfuscation to secure their apps and intellectual property. Although obfuscation provides protection, it presents an obstacle for a number of legitimate program analyses such as detection of app cloning and repackaging, malware detection, identification of third-party libraries, provenance analysis for digital forensics, and reverse engineering for test generation and performance analysis. If the obfuscator used to create an app can be identified, and if some details of the obfuscation process can be inferred, subsequent analyses can exploit this knowledge. Thus, it is desirable to be able to automatically analyze a given app and determine (1) whether it was obfuscated, (2) which obfuscator was used, and (3) how the obfuscator was configured. We have developed novel techniques to identify the obfuscator of an Android app for several widely-used obfuscation tools and for a number of their configuration options. We define the obfuscator identification problem and propose a solution based on machine learning. To the best of our knowledge, this is the first work to formulate and solve this problem. We identify a feature vector that represents the characteristics of the obfuscated code. We then implement a tool that extracts this feature vector from Dalvik bytecode and uses it to identify the obfuscator provenance information. We evaluate the proposed approach on real-world Android apps obfuscated with different obfuscators, under several configurations. Our experiments indicate that the approach identifies the obfuscator with about 97% accuracy and recognizes the configuration with more than 90% accuracy.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"184 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132623994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-20DOI: 10.1109/MOBILESoft.2017.4
Wooseok Lee, Dam Sunwoo, A. Gerstlauer, L. John
In mobile interactive web applications, energy-efficient quality-of-service (QoS) scheduling involves setting a deadline for the best user experience and providing just enough performance to minimize energy. Such performance-slacking approaches require precise performance adjustment using execution time prediction. However, prior prediction approaches suffer from prohibitive training due to extensive input data and manual source code instrumentation. In this paper, we propose a cloud-guided QoS and energy management approach that eliminates the prediction overhead by offloading it to cloud resources. Our approach pre-computes per-input execution time models by profiling web applications on dedicated mobile devices in the cloud. When mobile web applications request data to servers, both the data and its execution time models are delivered to users' mobile devices. Based on the delivered models, a performance control agent on the mobile device selects an operating point to meet the response time requirement. Experimental results show that, by offloading modeling and prediction overheads, our performance-slacking approach can provide average energy savings of 22% and 39% (and up to 89%) for two different timing budgets compared to an industry-quality approach.
{"title":"Cloud-Guided QoS and Energy Management for Mobile Interactive Web Applications","authors":"Wooseok Lee, Dam Sunwoo, A. Gerstlauer, L. John","doi":"10.1109/MOBILESoft.2017.4","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.4","url":null,"abstract":"In mobile interactive web applications, energy-efficient quality-of-service (QoS) scheduling involves setting a deadline for the best user experience and providing just enough performance to minimize energy. Such performance-slacking approaches require precise performance adjustment using execution time prediction. However, prior prediction approaches suffer from prohibitive training due to extensive input data and manual source code instrumentation. In this paper, we propose a cloud-guided QoS and energy management approach that eliminates the prediction overhead by offloading it to cloud resources. Our approach pre-computes per-input execution time models by profiling web applications on dedicated mobile devices in the cloud. When mobile web applications request data to servers, both the data and its execution time models are delivered to users' mobile devices. Based on the delivered models, a performance control agent on the mobile device selects an operating point to meet the response time requirement. Experimental results show that, by offloading modeling and prediction overheads, our performance-slacking approach can provide average energy savings of 22% and 39% (and up to 89%) for two different timing budgets compared to an industry-quality approach.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121021639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-20DOI: 10.1109/MOBILESoft.2017.36
Kevin Moran, R. Bonett, Carlos Bernal-Cárdenas, Brendan Otten, Daniel Park, D. Poshyvanyk
Bugs that surface in mobile applications can be difficult to reproduce and fix due to several confounding factors including the highly GUI-driven nature of mobile apps, varying contextual states, differing platform versions and device fragmentation. It is clear that developers need support in the form of automated tools that allow for more precise reporting of application defects in order to facilitate more efficient and effective bug fixes. In this paper, we present a tool aimed at supporting application testers and developers in the process of On-Device Bug Reporting. Our tool, called ODBR, leverages the uiautomator framework and low-level event stream capture to offer support for recording and replaying a series of input gesture and sensor events that describe a bug in an Android application.
{"title":"On-Device Bug Reporting for Android Applications","authors":"Kevin Moran, R. Bonett, Carlos Bernal-Cárdenas, Brendan Otten, Daniel Park, D. Poshyvanyk","doi":"10.1109/MOBILESoft.2017.36","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.36","url":null,"abstract":"Bugs that surface in mobile applications can be difficult to reproduce and fix due to several confounding factors including the highly GUI-driven nature of mobile apps, varying contextual states, differing platform versions and device fragmentation. It is clear that developers need support in the form of automated tools that allow for more precise reporting of application defects in order to facilitate more efficient and effective bug fixes. In this paper, we present a tool aimed at supporting application testers and developers in the process of On-Device Bug Reporting. Our tool, called ODBR, leverages the uiautomator framework and low-level event stream capture to offer support for recording and replaying a series of input gesture and sensor events that describe a bug in an Android application.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125954443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-20DOI: 10.1109/MOBILESoft.2017.26
Stephen Rodriguez
Storing data in the cloud is often a long and disputed process of ensuring that cloud service providers meet the clients minimum requirements. Often, this process can be costly, lack adequate results, and miss important security checks that are often overlooked. Therefore, I propose a new strategy for meeting cloud security requirements through using a dynamic parsing agent to encrypt outgoing data using a "multi-folded" encryption hierarchy. While there are limitations to being truly secure, such as those recognized by WhiteHat Security in their annual reports[1], I believe my unique architecture will prove to be ideal in allowing any mobile device (i.e. User) to define how their data is stored in the cloud. Although we have found this approach useful for the mobile space, we believe it can also be applied to a variety of different audiences in need of a cloud data encryption system.
{"title":"Using Parsing Agents as a Service for Data Privacy","authors":"Stephen Rodriguez","doi":"10.1109/MOBILESoft.2017.26","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.26","url":null,"abstract":"Storing data in the cloud is often a long and disputed process of ensuring that cloud service providers meet the clients minimum requirements. Often, this process can be costly, lack adequate results, and miss important security checks that are often overlooked. Therefore, I propose a new strategy for meeting cloud security requirements through using a dynamic parsing agent to encrypt outgoing data using a \"multi-folded\" encryption hierarchy. While there are limitations to being truly secure, such as those recognized by WhiteHat Security in their annual reports[1], I believe my unique architecture will prove to be ideal in allowing any mobile device (i.e. User) to define how their data is stored in the cloud. Although we have found this approach useful for the mobile space, we believe it can also be applied to a variety of different audiences in need of a cloud data encryption system.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127880349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-20DOI: 10.1109/MOBILESoft.2017.14
A. Rahman, Priysha Pradhan, Asif Partho, L. Williams
Android applications pose security and privacy risks for end-users. These risks are often quantified by performing dynamic analysis and permission analysis of the Android applications after release. Prediction of security and privacy risks associated with Android applications at early stages of application development, e.g. when the developer (s) are writing the code of the application, might help Android application developers in releasing applications to end-users that have less security and privacy risk. The goal of this paper is to aid Android application developers in assessing the security and privacy risk associated with Android applications by using static code metrics as predictors. In our paper, we consider security and privacy risk of Android application as how susceptible the application is to leaking private information of end-users and to releasing vulnerabilities. We investigate how effectively static code metrics that are extracted from the source code of Android applications, can be used to predict security and privacy risk of Android applications. We collected 21 static code metrics of 1,407 Android applications, and use the collected static code metrics to predict security and privacy risk of the applications. As the oracle of security and privacy risk, we used Androrisk, a tool that quantifies the amount of security and privacy risk of an Android application using analysis of Android permissions and dynamic analysis. To accomplish our goal, we used statistical learners such as, radial-based support vector machine (r-SVM). For r-SVM, we observe a precision of 0.83. Findings from our paper suggest that with proper selection of static code metrics, r-SVM can be used effectively to predict security and privacy risk of Android applications.
{"title":"Predicting Android Application Security and Privacy Risk with Static Code Metrics","authors":"A. Rahman, Priysha Pradhan, Asif Partho, L. Williams","doi":"10.1109/MOBILESoft.2017.14","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.14","url":null,"abstract":"Android applications pose security and privacy risks for end-users. These risks are often quantified by performing dynamic analysis and permission analysis of the Android applications after release. Prediction of security and privacy risks associated with Android applications at early stages of application development, e.g. when the developer (s) are writing the code of the application, might help Android application developers in releasing applications to end-users that have less security and privacy risk. The goal of this paper is to aid Android application developers in assessing the security and privacy risk associated with Android applications by using static code metrics as predictors. In our paper, we consider security and privacy risk of Android application as how susceptible the application is to leaking private information of end-users and to releasing vulnerabilities. We investigate how effectively static code metrics that are extracted from the source code of Android applications, can be used to predict security and privacy risk of Android applications. We collected 21 static code metrics of 1,407 Android applications, and use the collected static code metrics to predict security and privacy risk of the applications. As the oracle of security and privacy risk, we used Androrisk, a tool that quantifies the amount of security and privacy risk of an Android application using analysis of Android permissions and dynamic analysis. To accomplish our goal, we used statistical learners such as, radial-based support vector machine (r-SVM). For r-SVM, we observe a precision of 0.83. Findings from our paper suggest that with proper selection of static code metrics, r-SVM can be used effectively to predict security and privacy risk of Android applications.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115155318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-01DOI: 10.1109/MOBILESoft.2017.12
Tobias Dürschmid, Matthias Trapp, J. Döllner
Software product line development for Android apps is difficult due to an inflexible design of the Android framework. However, since mobile applications become more and more complex, increased code reuse and thus reduced time-to-market play an important role, which can be improved by software product lines. We propose five architectural styles for developing software product lines of Android apps: (1) activity extensions, (2) activity connectors, (3) dynamic preference entries, (4) decoupled definition of domain-specific behavior via configuration files, (5) feature model using Android resources. We demonstrate the benefits in an early case study using an image processing product line which enables more than 90% of code reuse.
{"title":"Towards Architectural Styles for Android App Software Product Lines","authors":"Tobias Dürschmid, Matthias Trapp, J. Döllner","doi":"10.1109/MOBILESoft.2017.12","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.12","url":null,"abstract":"Software product line development for Android apps is difficult due to an inflexible design of the Android framework. However, since mobile applications become more and more complex, increased code reuse and thus reduced time-to-market play an important role, which can be improved by software product lines. We propose five architectural styles for developing software product lines of Android apps: (1) activity extensions, (2) activity connectors, (3) dynamic preference entries, (4) decoupled definition of domain-specific behavior via configuration files, (5) feature model using Android resources. We demonstrate the benefits in an early case study using an image processing product line which enables more than 90% of code reuse.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"41 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117244064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-01DOI: 10.1109/MOBILESoft.2017.6
Li Li, Daoyuan Li, Tegawendé F. Bissyandé, Jacques Klein, Haipeng Cai, D. Lo, Yves Le Traon
To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to provide a framework for dissecting malware and locating malicious program fragments within app code in order to build a comprehensive dataset of malicious samples. Towards addressing this need, we propose in this work a tool-based approach called HookRanker, which provides ranked lists of potentially malicious packages based on the way malware behaviour code is triggered. With experiments on a ground truth set of piggybacked apps, we are able to automatically locate the malicious packages from piggybacked Android apps with an accuracy of 83.6% in verifying the top five reported items.
{"title":"Automatically Locating Malicious Packages in Piggybacked Android Apps","authors":"Li Li, Daoyuan Li, Tegawendé F. Bissyandé, Jacques Klein, Haipeng Cai, D. Lo, Yves Le Traon","doi":"10.1109/MOBILESoft.2017.6","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.6","url":null,"abstract":"To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to provide a framework for dissecting malware and locating malicious program fragments within app code in order to build a comprehensive dataset of malicious samples. Towards addressing this need, we propose in this work a tool-based approach called HookRanker, which provides ranked lists of potentially malicious packages based on the way malware behaviour code is triggered. With experiments on a ground truth set of piggybacked apps, we are able to automatically locate the malicious packages from piggybacked Android apps with an accuracy of 83.6% in verifying the top five reported items.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132498811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-27DOI: 10.1109/MOBILESoft.2017.28
Danilo Dominguez Perez, Wei Le
One of the challenges of analyzing, testing and debugging Android apps is that the potential execution orders of callbacks are missing from the apps' source code. However, bugs, vulnerabilities and refactoring transformations have been found to be related to callback sequences. Existing work on control flow analysis of Android apps have mainly focused on analyzing GUI events. GUI events, although being a key part of determining control flow of Android apps, do not offer a complete picture. Our observation is that orthogonal to GUI events, the Android API calls also play an important role in determining the order of callbacks. In the past, such control flow information has been modeled manually. This paper presents a complementary solution of constructing program paths for Android apps. We proposed a specification technique, called Predicate Callback Summary (PCS), that represents the callback control flow information (including callback sequences as well as the conditions under which the callbacks are invoked) in Android API methods and developed static analysis techniques to automatically compute and apply such summaries to construct apps' callback sequences. Our experiments show that by applying PCSs, we are able to construct Android apps' control flow graphs, including inter callback relations, and also to detect infeasible paths involving multiple callbacks. Such control flow information can help program analysis and testing tools to report more precise results. Our detailed experimental data is available at: http://www.cs.iastate.edu/~weile/toolsdata/SummarizeAndroidFramework/lithium.html.
{"title":"Generating Predicate Callback Summaries for the Android Framework","authors":"Danilo Dominguez Perez, Wei Le","doi":"10.1109/MOBILESoft.2017.28","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.28","url":null,"abstract":"One of the challenges of analyzing, testing and debugging Android apps is that the potential execution orders of callbacks are missing from the apps' source code. However, bugs, vulnerabilities and refactoring transformations have been found to be related to callback sequences. Existing work on control flow analysis of Android apps have mainly focused on analyzing GUI events. GUI events, although being a key part of determining control flow of Android apps, do not offer a complete picture. Our observation is that orthogonal to GUI events, the Android API calls also play an important role in determining the order of callbacks. In the past, such control flow information has been modeled manually. This paper presents a complementary solution of constructing program paths for Android apps. We proposed a specification technique, called Predicate Callback Summary (PCS), that represents the callback control flow information (including callback sequences as well as the conditions under which the callbacks are invoked) in Android API methods and developed static analysis techniques to automatically compute and apply such summaries to construct apps' callback sequences. Our experiments show that by applying PCSs, we are able to construct Android apps' control flow graphs, including inter callback relations, and also to detect infeasible paths involving multiple callbacks. Such control flow information can help program analysis and testing tools to report more precise results. Our detailed experimental data is available at: http://www.cs.iastate.edu/~weile/toolsdata/SummarizeAndroidFramework/lithium.html.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121612870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-24DOI: 10.1109/MOBILESoft.2017.30
Matias Martinez, S. Lecomte
During last ten years, the number of smartphonesand mobile applications has been constantly growing. Android, iOS and Windows Mobile are three mobile platforms that coveralmost all smartphones in the world in 2017. Developing a mobileapp involves first to choose the platforms the app will run, andthen to develop specific solutions (i.e., native apps) for eachchosen platform using platform-related toolkits such as AndroidSDK. A cross-platform mobile application is an app that runs ontwo or more mobile platforms. Several frameworks have beenproposed to simplify the development of cross-platform mobileapplications and to reduce development and maintenance costs. They are called cross-platform mobile app development frameworks. However, to our knowledge, the life-cycle and the quality of cross-platformsmobile applications built using those frameworks havenot been studied in depth. Our main goal is to first study theprocesses of development and maintenance of mobile applicationsbuilt using cross-platform mobile app development frameworks, focusing particularly on the bug-fixing activity. Then, we aim atdefining tools for automated repairing bugs from cross-platformmobile applications.
{"title":"Towards the Quality Improvement of Cross-Platform Mobile Applications","authors":"Matias Martinez, S. Lecomte","doi":"10.1109/MOBILESoft.2017.30","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.30","url":null,"abstract":"During last ten years, the number of smartphonesand mobile applications has been constantly growing. Android, iOS and Windows Mobile are three mobile platforms that coveralmost all smartphones in the world in 2017. Developing a mobileapp involves first to choose the platforms the app will run, andthen to develop specific solutions (i.e., native apps) for eachchosen platform using platform-related toolkits such as AndroidSDK. A cross-platform mobile application is an app that runs ontwo or more mobile platforms. Several frameworks have beenproposed to simplify the development of cross-platform mobileapplications and to reduce development and maintenance costs. They are called cross-platform mobile app development frameworks. However, to our knowledge, the life-cycle and the quality of cross-platformsmobile applications built using those frameworks havenot been studied in depth. Our main goal is to first study theprocesses of development and maintenance of mobile applicationsbuilt using cross-platform mobile app development frameworks, focusing particularly on the bug-fixing activity. Then, we aim atdefining tools for automated repairing bugs from cross-platformmobile applications.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121447725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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