Towards Measuring Real-World Performance of Android Devices

Bissig, Pascal; Brunner, Gino; Gubler, Florian; Wattenhofer, Roger; Zingg, Andreas

doi:10.1109/aiccsa.2018.8612818

Cited by 2 publications

(8 citation statements)

References 3 publications

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“…The average number of data categories used per surveyed study is 2.66. Furthermore, among the 15 studies on the PTS research theme, 12 studies excluding three studies [79,138,86] used only types of data corresponding to interaction sensing data. In the three studies, Bissi et al [79] and Xiang et al [138] commonly collected device operation status data for performance measurement and management research, in addition to data types corresponding to interaction sensing data.…”

Section: Programming and Testing Support (Pts) Research Themesmentioning

confidence: 99%

“…Furthermore, among the 15 studies on the PTS research theme, 12 studies excluding three studies [79,138,86] used only types of data corresponding to interaction sensing data. In the three studies, Bissi et al [79] and Xiang et al [138] commonly collected device operation status data for performance measurement and management research, in addition to data types corresponding to interaction sensing data. In surveyed studies corresponding to the GAT (i.e., GUI Automated Testing), CT (i.e., Crowdsourced Testing), APS (i.e., Automated Programming Support), and TRSGT (i.e., Test Recorder and Script Generator Technique) sub-research themes, except for the microphone data used in Li et al [86], app usage pattern, user interaction, and notification data, which are data types that belong to interaction sensing data, are used in more than one paper.…”

Section: Programming and Testing Support (Pts) Research Themesmentioning

confidence: 99%

“…Click (including tapping) [17,18,87,88,30,28,70,69,68,67,66,132,73,74,75,78,77,84,86,79,82,83,81,80,139,15] 26…”

Section: Types Of Touch Interaction Ref Nummentioning

confidence: 99%

“…Among 45 studies using interaction data, 18 studies use terms related to type of touch interaction (i.e., click, long click, typing, scroll, pinch, swipe) to describe the type of touch interaction used in References [17,21,37,87,88,103,104,15,70,69,61,68,67,139,73,74,77,80]. The 16 studies which are the names of Accessibili-tyEvent related to the type of touch interaction (TYPES_VIEW_CLICKED, TYPES_VIEW_LONG_CLICKED, TYPES_VIEW_TEXT_CHANGED, TYPES_VIEW_SCOLLED) were used to describe the touch interaction used in References [18,30,28,66,49,132,57,58,75,84,86,79,82,83,81,78]. In the remaining 11 studies, general term expressions were described such as "user action", "touch event", "all UI events", "touch event", "input action", and "gesture action" were used in References [107,29,32,35,71,65,140,158,54,55,…”

Section: Types Of Touch Interaction Ref Nummentioning

confidence: 99%

“…• Testing and programming support fields, such as graphical user interface (GUI) automated testing, crowdsourced system testing, programming support for developer, and app performance measurement [73,74,75,76,77,78,79,80,81,82,83,84,85,86].…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Survey on Android API Usage for Data-Driven Analytics with Smartphones

Lee,

Park,

Lee

2021

Preprint

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Recently, there has been an increase in industrial and academic research on data-driven analytics with smartphones based on the collection of app usage patterns and surrounding context data. The Android mobile operating system utilizes Usage Statistics API (US API) and Accessibility Service API (AS API) as representative APIs to passively collect app usage data. These APIs are used for various research purposes as they can collect app usage patterns (e.g., app status, usage time, app name, user interaction state, and smartphone use state) and fine-grained data (e.g., user interface elements & hierarchy and user interaction type & target & time) of each application. In addition, other sensing APIs help to collect the user's surroundings context (location, network, ambient environment) and device state data, along with AS/US API. In this review, we provide insights on the types of mobile usage and sensor data that can be collected for each research purpose by considering Android built-in APIs and sensors (AS/US API, and other sensing APIs). Moreover, we classify the research purposes of the surveyed papers into four categories and 17 sub-categories, and create a hierarchical structure for data classification, comprising three layers. We present the important trends in the usage of Android's built-in APIs and sensors, including AS/US API, the types of data collected using the presented APIs, and discuss the utilization of mobile usage and sensor data in future research.

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Section: Programming and Testing Support (Pts) Research Themesmentioning

confidence: 99%

Section: Programming and Testing Support (Pts) Research Themesmentioning

confidence: 99%

“…Click (including tapping) [17,18,87,88,30,28,70,69,68,67,66,132,73,74,75,78,77,84,86,79,82,83,81,80,139,15] 26…”

Section: Types Of Touch Interaction Ref Nummentioning

confidence: 99%

Section: Types Of Touch Interaction Ref Nummentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Systematic Survey on Android API Usage for Data-Driven Analytics with Smartphones

Lee,

Park,

Lee

2021

Preprint

View full text Add to dashboard Cite

show abstract

A Systematic Survey on Android API Usage for Data-driven Analytics with Smartphones

2022

View full text Add to dashboard Cite

Recent industrial and academic research has focused on data-driven analytics with smartphones by collecting user interaction, context, and device systems data through Application Programming Interfaces (APIs) and sensors. The Android OS provides various APIs to collect such mobile usage and sensor data for third-party developers. Usage Statistics API (US API) and Accessibility Service API (AS API) are representative Android APIs for collecting app usage data and are used for various research purposes as they can collect fine-grained interaction data (e.g., app usage history, user interaction type). Furthermore, other sensor APIs help to collect a user’s context and device state data, along with AS/US APIs. This review investigates mobile usage and sensor data-driven research using AS/US APIs, by categorizing the research purposes and the data types. In this paper, the surveyed studies are classified as follows: five themes and 21 subthemes, and a four-layer hierarchical data classification structure. This allows us to identify a data usage trend and derive insight into data collection according to research purposes. Several limitations and future research directions of mobile usage and sensor data-driven analytics research are discussed, including the impact of changes in the Android API versions on research, the privacy and data quality issues, and the mitigation of reproducibility risks with standardized data typology.

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Towards Measuring Real-World Performance of Android Devices

Cited by 2 publications

References 3 publications

A Systematic Survey on Android API Usage for Data-Driven Analytics with Smartphones

A Systematic Survey on Android API Usage for Data-Driven Analytics with Smartphones

A Systematic Survey on Android API Usage for Data-driven Analytics with Smartphones

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