Measuring quality of Web users experience (WebQoE) faces the following trade-off. On the one hand, current practice is to resort to metrics, such as the document completion time (onLoad), that are simple to measure though knowingly inaccurate. On the other hand, there are metrics, like Google’s SpeedIndex, that are better correlated with the actual user experience, but are quite complex to evaluate and, as such, relegated to lab experiments. In this paper, we first provide a comprehensive state of the art on the metrics and tools available for WebQoE assessment. We then apply these metrics to a representative dataset (the Alexa top-100 webpages) to better illustrate their similarities, differences, advantages, and limitations. We next introduce novel metrics, inspired by Google’s SpeedIndex, that offer significant advantage in terms of computational complexity, while maintaining a high correlation with the SpeedIndex. These properties make our proposed metrics highly relevant and of practical use.

Measuring the quality of experience of web users / Bocchi, Enrico; De Cicco, Luca; Rossi, Dario. - In: COMPUTER COMMUNICATION REVIEW. - ISSN 0146-4833. - 46:4(2016), pp. 8-13. [10.1145/3027947.3027949]

Measuring the quality of experience of web users

BOCCHI, ENRICO;
2016

Abstract

Measuring quality of Web users experience (WebQoE) faces the following trade-off. On the one hand, current practice is to resort to metrics, such as the document completion time (onLoad), that are simple to measure though knowingly inaccurate. On the other hand, there are metrics, like Google’s SpeedIndex, that are better correlated with the actual user experience, but are quite complex to evaluate and, as such, relegated to lab experiments. In this paper, we first provide a comprehensive state of the art on the metrics and tools available for WebQoE assessment. We then apply these metrics to a representative dataset (the Alexa top-100 webpages) to better illustrate their similarities, differences, advantages, and limitations. We next introduce novel metrics, inspired by Google’s SpeedIndex, that offer significant advantage in terms of computational complexity, while maintaining a high correlation with the SpeedIndex. These properties make our proposed metrics highly relevant and of practical use.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2665104
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