The Experts below are selected from a list of 8049 Experts worldwide ranked by ideXlab platform
Andreas Zeller - One of the best experts on this subject based on the ideXlab platform.
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learning User Interface Element interactions
International Symposium on Software Testing and Analysis, 2019Co-Authors: Christian Degott, Nataniel P. Borges, Andreas ZellerAbstract:When generating tests for graphical User Interfaces, one central problem is to identify how individual UI Elements can be interacted with—clicking, long- or right-clicking, swiping, dragging, typing, or more. We present an approach based on reinforcement learning that automatically learns which interactions can be used for which Elements, and uses this information to guide test generation. We model the problem as an instance of the multi-armed bandit problem (MAB problem) from probability theory, and show how its traditional solutions work on test generation, with and without relying on previous knowledge. The resulting guidance yields higher coverage. In our evaluation, our approach shows improvements in statement coverage between 18% (when not using any previous knowledge) and 20% (when reusing previously generated models).
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ISSTA - Learning User Interface Element interactions
Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis - ISSTA 2019, 2019Co-Authors: Christian Degott, Nataniel P. Borges, Andreas ZellerAbstract:When generating tests for graphical User Interfaces, one central problem is to identify how individual UI Elements can be interacted with—clicking, long- or right-clicking, swiping, dragging, typing, or more. We present an approach based on reinforcement learning that automatically learns which interactions can be used for which Elements, and uses this information to guide test generation. We model the problem as an instance of the multi-armed bandit problem (MAB problem) from probability theory, and show how its traditional solutions work on test generation, with and without relying on previous knowledge. The resulting guidance yields higher coverage. In our evaluation, our approach shows improvements in statement coverage between 18% (when not using any previous knowledge) and 20% (when reusing previously generated models).
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ICSE (Companion Volume) - Detecting behavior anomalies in graphical User Interfaces
2017 IEEE ACM 39th International Conference on Software Engineering Companion (ICSE-C), 2017Co-Authors: Vitalii Avdiienko, Konstantin Kuznetsov, Isabelle Rommelfanger, Andreas Rau, Alessandra Gorla, Andreas ZellerAbstract:When interacting with User Interfaces, do Users always get what they expect? For each User Interface Element in thousands of Android apps, we extracted the Android APIs they invoke as well as the text shown on their screen. This association allows us to detect outliers: User Interface Elements whose text, context or icon suggests one action, but which actually are tied to other actions. In our evaluation of tens of thousands of UI Elements, our BACKSTAGE prototype discovered misleading random UI Elements with an accuracy of 73%.
Christian Degott - One of the best experts on this subject based on the ideXlab platform.
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learning User Interface Element interactions
International Symposium on Software Testing and Analysis, 2019Co-Authors: Christian Degott, Nataniel P. Borges, Andreas ZellerAbstract:When generating tests for graphical User Interfaces, one central problem is to identify how individual UI Elements can be interacted with—clicking, long- or right-clicking, swiping, dragging, typing, or more. We present an approach based on reinforcement learning that automatically learns which interactions can be used for which Elements, and uses this information to guide test generation. We model the problem as an instance of the multi-armed bandit problem (MAB problem) from probability theory, and show how its traditional solutions work on test generation, with and without relying on previous knowledge. The resulting guidance yields higher coverage. In our evaluation, our approach shows improvements in statement coverage between 18% (when not using any previous knowledge) and 20% (when reusing previously generated models).
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ISSTA - Learning User Interface Element interactions
Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis - ISSTA 2019, 2019Co-Authors: Christian Degott, Nataniel P. Borges, Andreas ZellerAbstract:When generating tests for graphical User Interfaces, one central problem is to identify how individual UI Elements can be interacted with—clicking, long- or right-clicking, swiping, dragging, typing, or more. We present an approach based on reinforcement learning that automatically learns which interactions can be used for which Elements, and uses this information to guide test generation. We model the problem as an instance of the multi-armed bandit problem (MAB problem) from probability theory, and show how its traditional solutions work on test generation, with and without relying on previous knowledge. The resulting guidance yields higher coverage. In our evaluation, our approach shows improvements in statement coverage between 18% (when not using any previous knowledge) and 20% (when reusing previously generated models).
Nataniel P. Borges - One of the best experts on this subject based on the ideXlab platform.
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learning User Interface Element interactions
International Symposium on Software Testing and Analysis, 2019Co-Authors: Christian Degott, Nataniel P. Borges, Andreas ZellerAbstract:When generating tests for graphical User Interfaces, one central problem is to identify how individual UI Elements can be interacted with—clicking, long- or right-clicking, swiping, dragging, typing, or more. We present an approach based on reinforcement learning that automatically learns which interactions can be used for which Elements, and uses this information to guide test generation. We model the problem as an instance of the multi-armed bandit problem (MAB problem) from probability theory, and show how its traditional solutions work on test generation, with and without relying on previous knowledge. The resulting guidance yields higher coverage. In our evaluation, our approach shows improvements in statement coverage between 18% (when not using any previous knowledge) and 20% (when reusing previously generated models).
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ISSTA - Learning User Interface Element interactions
Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis - ISSTA 2019, 2019Co-Authors: Christian Degott, Nataniel P. Borges, Andreas ZellerAbstract:When generating tests for graphical User Interfaces, one central problem is to identify how individual UI Elements can be interacted with—clicking, long- or right-clicking, swiping, dragging, typing, or more. We present an approach based on reinforcement learning that automatically learns which interactions can be used for which Elements, and uses this information to guide test generation. We model the problem as an instance of the multi-armed bandit problem (MAB problem) from probability theory, and show how its traditional solutions work on test generation, with and without relying on previous knowledge. The resulting guidance yields higher coverage. In our evaluation, our approach shows improvements in statement coverage between 18% (when not using any previous knowledge) and 20% (when reusing previously generated models).
Sebastian Schrittwieser - One of the best experts on this subject based on the ideXlab platform.
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Typosquatting for Fun and Profit: Cross-Country Analysis of Pop-Up Scam
Journal of Cyber Security and Mobility, 2020Co-Authors: Tobias Dam, Lukas Daniel Klausner, Sebastian SchrittwieserAbstract:Today, many different types of scams can be found on the internet. Online criminals are always finding new creative ways to trick internet Users, be it in the form of lottery scams, downloading scam apps for smartphones or fake gambling websites. This paper presents a large-scale study on one particular delivery method of online scam: pop-up scam on typosquatting domains. Typosquatting describes the concept of registering domains which are very similar to existing ones while deliberately containing common typing errors; these domains are then used to trick online Users while under the belief of browsing the intended website. Pop-up scam uses JavaScript alert boxes to present a message which attracts the User’s attention very effectively, as they are a blocking User Interface Element. Our study among typosquatting domains derived from the Majestic Million list utilising an Austrian IP address revealed on 1219 distinct typosquatting URLs a total of 2577 pop-up messages, out of which 1538 were malicious. Approximately a third of those distinct URLs (403) were targeted and displayed pop-up messages to one specific HTTP User agent only. Based on our scans, we present an in-depth analysis as well as a detailed classification of different targeting parameters (User agent and language) which triggered varying kinds of pop-up scams. Furthermore, we expound the differences of current pop-up scam characteristics in comparison with a previous scan performed in late 2018 and examine the use of IDN homograph attacks as well as the application of message localisation using additional scans with IP addresses from the United States and Japan.
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Large-Scale Analysis of Pop-Up Scam on Typosquatting URLs
Proceedings of the 14th International Conference on Availability Reliability and Security, 2019Co-Authors: Tobias Dam, Lukas Daniel Klausner, Damjan Buhov, Sebastian SchrittwieserAbstract:Today, many different types of scams can be found on the internet. Online criminals are always finding new creative ways to trick internet Users, be it in the form of lottery scams, downloading scam apps for smartphones or fake gambling websites. This paper presents a large-scale study on one particular delivery method of online scam: pop-up scam on typosquatting domains. Typosquatting describes the concept of registering domains which are very similar to existing ones while deliberately containing common typing errors; these domains are then used to trick online Users while under the belief of browsing the intended website. Pop-up scam uses JavaScript alert boxes to present a message which attracts the User's attention very effectively, as they are a blocking User Interface Element. Our study among typosquatting domains derived from the Alexa Top 1 Million list revealed on 8255 distinct typosquatting URLs a total of 9857 pop-up messages, out of which 8828 were malicious. The vast majority of those distinct URLs (7176) were targeted and displayed pop-up messages to one specific HTTP User agent only. Based on our scans, we present an in-depth analysis as well as a detailed classification of different targeting parameters (User agent and language) which triggered varying kinds of pop-up scams.
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ARES - Large-Scale Analysis of Pop-Up Scam on Typosquatting URLs
2019Co-Authors: Tobias Dam, Lukas Daniel Klausner, Damjan Buhov, Sebastian SchrittwieserAbstract:Today, many different types of scams can be found on the internet. Online criminals are always finding new creative ways to trick internet Users, be it in the form of lottery scams, downloading scam apps for smartphones or fake gambling websites. This paper presents a large-scale study on one particular delivery method of online scam: pop-up scam on typosquatting domains. Typosquatting describes the concept of registering domains which are very similar to existing ones while deliberately containing common typing errors; these domains are then used to trick online Users while under the belief of browsing the intended website. Pop-up scam uses JavaScript alert boxes to present a message which attracts the User's attention very effectively, as they are a blocking User Interface Element. Our study among typosquatting domains derived from the Alexa Top 1 Million list revealed on 8 255 distinct typosquatting URLs a total of 9 857 pop-up messages, out of which 8 828 were malicious. The vast majority of those distinct URLs (7 176) were targeted and displayed pop-up messages to one specific HTTP User agent only. Based on our scans, we present an in-depth analysis as well as a detailed classification of different targeting parameters (User agent and language) which triggered varying kinds of pop-up scams.
Jan Borchers - One of the best experts on this subject based on the ideXlab platform.
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CHI - Pinstripe: eyes-free continuous input on interactive clothing
Proceedings of the 2011 annual conference on Human factors in computing systems - CHI '11, 2011Co-Authors: Thorsten Karrer, Moritz Wittenhagen, Florian Heller, Leonhard Lichtschlag, Jan BorchersAbstract:We present Pinstripe, a textile User Interface Element for eyes-free, continuous value input on smart garments that uses pinching and rolling a piece of cloth between your fingers. The input granularity can be controlled in a natural way by varying the amount of cloth pinched. Pinstripe input Elements physically consist of fields of parallel conductive lines sewn onto the fabric. This way, they can be invisible, and can be included across large areas of a garment. Pinstripe also addresses several problems previously identified in the placement and operation of textile UI Elements on smart clothing. Two User studies evaluate ideal placement and orientation of Pinstripe Elements on the Users' garments as well as acceptance and perceived ease of use of this novel textile input technique.
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UIST (Adjunct Volume) - Pinstripe: eyes-free continuous input anywhere on interactive clothing
Adjunct proceedings of the 23nd annual ACM symposium on User interface software and technology - UIST '10, 2010Co-Authors: Thorsten Karrer, Moritz Wittenhagen, Florian Heller, Jan BorchersAbstract:We present Pinstripe, a textile User Interface Element for eyes-free, continuous value input on smart garments that uses pinching and rolling a piece of cloth between your fingers. Input granularity can be controlled by the amount of cloth pinched. Pinstripe input Elements are invisible, and can be included across large areas of a garment. Pinstripe thus addresses several problems previously identified in the placement and operation of textile UI Elements on smart clothing.
