Measurement-based Experiments on the Mobile Web: A Systematic Mapping Study
The mobile Web is growing as more and more people use a smart device to access online services. This rapid growth of mobile Web usage is accompanied by the evolution of the mobile Web browser as a fully fledged software platform. Due to these two trends, the expectations of users in terms of quality of experience (QoE) when browsing the Web on their mobile device has increased drastically. As a result, the number of studies using measurement-based experiments to investigate the factors influencing QoE has grown.
However, conducting measurement-based experiments on the mobile Web is not a trivial task as it requires a significant experience and knowledge about both technical and methodological aspects. Unfortunately, there is no systematic study on the state of the art of conducting measurement-based experiments on the mobile Web that could guide researchers and practitioners when planning and performing such experiments.
The goal of this work is to build a map of existing studies that conduct measurement-based experiments on the mobile Web. In total 640 potentially relevant studies are identified. After a rigorous selection procedure the set of primary studies consists of 28 papers from which we extracted data and gathered insights. Specifically, we investigate on (i) which metrics are collected, how they are measured, and how they are analysed, (ii) the platforms on which the experiments are run, (iii) what subjects are used, and (iv) the used tools and environments under which the experiments are run.
This study benefits researchers and practitioners by presenting common techniques, empirical practices, and tools to properly conduct measurement-based experiments on the mobile Web.
Wed 23 JunDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
13:00 - 14:30 | Software Quality EASE 2021 / Vision and Emerging Results Track at Zoom Chair(s): Irit Hadar University of Haifa | ||
13:00 22mFull-paper | Detection and Elimination of Systematic Labeling Bias in Code Reviewer Recommendation Systems EASE 2021 K. Ayberk Tecimer Technical University of Munich, Eray Tüzün Bilkent University, Hamdi Dibeklioğlu Bilkent University, Hakan Erdogmus Carnegie Mellon University Pre-print | ||
13:22 22mFull-paper | From Blackboard to the Office: A Look into how Practitioners Perceive Software Testing Education EASE 2021 Luana Martins Federal University of Bahia, Vinícius Brito Federal University of Bahia, Daniela Feitosa Federal University of Bahia, Larissa Rocha Federal University of Bahia / State University of Feira de Santana, Heitor Augustus Xavier Costa Federal University of Lavras, Ivan Machado Federal University of Bahia Pre-print | ||
13:45 22mFull-paper | Measurement-based Experiments on the Mobile Web: A Systematic Mapping Study EASE 2021 Pre-print | ||
14:07 22mVision and Emerging Results | Open Data-driven Usability Improvements of Static Code Analysis and its Challenges Vision and Emerging Results Track Emma Söderberg Lund University, Luke Church University of Cambridge | Lund University | Lark Systems, Martin Höst Lund University Pre-print |