TY - GEN
T1 - A Framework for the Automatic Execution of Measurement-based Experiments on Android Devices
AU - Malavolta, Ivano
AU - Grua, Eoin Martino
AU - Lam, Cheng Yu
AU - De Vries, Randy
AU - Tan, Franky
AU - Zielinski, Eric
AU - Peters, Michael
AU - Kaandorp, Luuk
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/9
Y1 - 2020/9
N2 - Conducting measurement-based experiments is fundamental for assessing the quality of Android apps in terms of, e.g., energy consumption, CPU, and memory usage. However, orchestrating such experiments is not trivial as it requires large boilerplate code, careful setup of measurement tools, and the adoption of various empirical best practices scattered across the literature. All together, those factors are slowing down the scientific advancement and harming experiments' replicability in the mobile software engineering area. In this paper we present Android Runner (AR), a framework for automatically executing measurement-based experiments on native and web apps running on Android devices. In AR, an experiment is defined once in a descriptive fashion, and then its execution is fully automatic, customizable, and replicable. AR is implemented in Python and it can be extended with third-party profilers. AR has been used in more than 25 scientific studies primarily targeting performance and energy efficiency.
AB - Conducting measurement-based experiments is fundamental for assessing the quality of Android apps in terms of, e.g., energy consumption, CPU, and memory usage. However, orchestrating such experiments is not trivial as it requires large boilerplate code, careful setup of measurement tools, and the adoption of various empirical best practices scattered across the literature. All together, those factors are slowing down the scientific advancement and harming experiments' replicability in the mobile software engineering area. In this paper we present Android Runner (AR), a framework for automatically executing measurement-based experiments on native and web apps running on Android devices. In AR, an experiment is defined once in a descriptive fashion, and then its execution is fully automatic, customizable, and replicable. AR is implemented in Python and it can be extended with third-party profilers. AR has been used in more than 25 scientific studies primarily targeting performance and energy efficiency.
KW - n/a
UR - http://www.scopus.com/inward/record.url?scp=85100623383&partnerID=8YFLogxK
U2 - 10.1145/3417113.3422184
DO - 10.1145/3417113.3422184
M3 - Conference contribution
AN - SCOPUS:85100623383
T3 - Proceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering Workshops, ASEW 2020
SP - 61
EP - 66
BT - Proceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering Workshops, ASEW 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 35th IEEE/ACM International Conference on Automated Software Engineering Workshops, ASEW 2020
Y2 - 22 September 2020 through 25 September 2020
ER -
