Browsing by Author "Chu, Edward T. -H."

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Benchmarking handheld graphical user interface: Smoothness quality of experience
(2018-05-01)
Lin, Ying-Dar
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Chu, Edward T. -H.
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Wen, Chien-Ling
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Lai, Yuan-Cheng
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Chen, I-Ching
With the rapid growth of smartphones in the market, the smoothness of smartphones, how quickly and well smartphones react to a user's input, becomes a crucial factor consumers consider when making buying decisions. However, there is no benchmark for comparing the smoothness of one phone against another. In this paper, a handheld smoothness evaluation over regression (HSER) model was developed to make a fair evaluation. A video was first made and the several key indexes were extracted to represent behavior-based smoothness quality of services (BQoS). We built up a relationship between BQoS and behavior-based smoothness quality of experience (BQoE), and converted BQoE to handheld smoothness quality of experience. Our experiment results show that maximal frame interval and number of frame intervals are the two most critical indexes that indicate smoothness. The proposed HSER model is able to fairly evaluate the smoothness of smartphones because the error rate of the HSER model is less than 9% for a single behavior.
Improving the Accuracy of Automated GUI Testing for Embedded Systems
(2014-01-01)
Lin, Ying-Dar
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Chu, Edward T. -H.
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Yu, Shang-Che
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Lai, Yuan-Cheng
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交大名義發表
On the Accuracy, Efficiency, and Reusability of Automated Test Oracles for Android Devices
(2014-10-01)
Lin, Ying-Dar
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Rojas, Jose F.
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Chu, Edward T. -H.
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Lai, Yuan-Cheng
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資訊工程學系
Automated GUI testing consists of simulating user events and validating the changes in the GUI in order to determine if an Android application meets specifications. Traditional record-replay testing tools mainly focus on facilitating the test case writing process but not the replay and verification process. The accuracy of testing tools degrades significantly when the device under test (DUT) is under heavy load. In order to improve the accuracy, our previous work, SPAG, uses event batching and smart wait function to eliminate the uncertainty of the replay process and adopts GUI layout information to verify the testing results. SPAG maintains an accuracy of up to 99.5 percent and outperforms existing methods. In this work, we propose smart phone automated GUI testing tool with camera (SPAG-C), an extension of SPAG, to test an Android hardware device. Our goal is to further reduce the time required to record test cases and increase reusability of the test oracle without compromising test accuracy. In the record stage, SPAG captures screenshots from device\'s frame buffer and writes verification commands into the test case. Unlike SPAG, SPAG-C captures the screenshots from an external camera instead of frame buffer. In the replay stage, SPAG-C automatically performs image comparison while SPAG simply performs a string comparison to verify the test results. In order to make SPAG-C reusable for different devices and to allow better synchronization at the time of capturing images, we develop a new architecture that uses an external camera and Web services to decouple the test oracle. Our experiments show that recording a test case using SPAG-C\'s automatic verification is as fast as SPAG\'s but more accurate. Moreover, SPAG-C is 50 to 75 percent faster than SPAG in achieving the same test accuracy. With reusability, SPAG-C reduces the testing time from days to hours for heterogeneous devices.
Time-and-Energy-Aware Computation Offloading in Handheld Devices to Coprocessors and Clouds
(2015-06-01)
Lin, Ying-Dar
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Chu, Edward T. -H.
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Lai, Yuan-Cheng
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Huang, Ting-Jun
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資訊工程學系
Running sophisticated software on smart phones could result in poor performance and shortened battery lifetime because of their limited resources. Recently, offloading computation workload to the cloud has become a promising solution to enhance both performance and battery life of smart phones. However, it also consumes both time and energy to upload data or programs to the cloud and retrieve the results from the cloud. In this paper, we develop an offloading framework, named Ternary Decision Maker (TDM), which aims to shorten response time and reduce energy consumption at the same time. Unlike previous works, our targets of execution include an on-board CPU, an on-board GPU, and a cloud, all of which combined provide a more flexible execution environment for mobile applications. We conducted a real-world application, i.e., matrix multiplication, in order to evaluate the performance of TDM. According to our experimental results, TDM has less false offloading decision rate than existing methods. In addition, by offloading modules, our method can achieve, at most, 75% savings in execution time and 56% in battery usage.