TY - GEN
T1 - Automated construction of energy test oracles for Android
AU - Jabbarvand, Reyhaneh
AU - Mehralian, Forough
AU - Malek, Sam
N1 - Funding Information:
This work was supported in part by awards 1823262 and 1618132 from the National Science Foundation and a Google PhD Fellowship.
Publisher Copyright:
© 2020 Owner/Author.
PY - 2020/11/8
Y1 - 2020/11/8
N2 - Energy efficiency is an increasingly important quality attribute for software, particularly for mobile apps. Just like any other software attribute, energy behavior of mobile apps should be properly tested prior to their release. However, mobile apps are riddled with energy defects, as currently there is a lack of proper energy testing tools. Indeed, energy testing is a fledgling area of research and recent advances have mainly focused on test input generation. This paper presents ACETON, the first approach aimed at solving the oracle problem for testing the energy behavior of mobile apps. ACETON employs Deep Learning to automatically construct an oracle that not only determines whether a test execution reveals an energy defect, but also the type of energy defect. By carefully selecting features that can be monitored on any app and mobile device, we are assured the oracle constructed using ACETON is highly reusable. Our experiments show that the oracle produced by ACETON is both highly accurate, achieving an overall precision and recall of 99%, and efficient, detecting the existence of energy defects in only 37 milliseconds on average.
AB - Energy efficiency is an increasingly important quality attribute for software, particularly for mobile apps. Just like any other software attribute, energy behavior of mobile apps should be properly tested prior to their release. However, mobile apps are riddled with energy defects, as currently there is a lack of proper energy testing tools. Indeed, energy testing is a fledgling area of research and recent advances have mainly focused on test input generation. This paper presents ACETON, the first approach aimed at solving the oracle problem for testing the energy behavior of mobile apps. ACETON employs Deep Learning to automatically construct an oracle that not only determines whether a test execution reveals an energy defect, but also the type of energy defect. By carefully selecting features that can be monitored on any app and mobile device, we are assured the oracle constructed using ACETON is highly reusable. Our experiments show that the oracle produced by ACETON is both highly accurate, achieving an overall precision and recall of 99%, and efficient, detecting the existence of energy defects in only 37 milliseconds on average.
KW - Android
KW - Deep Learning
KW - Green Software Engineering
KW - Software Testing
KW - Test Oracle
UR - http://www.scopus.com/inward/record.url?scp=85097175699&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097175699&partnerID=8YFLogxK
U2 - 10.1145/3368089.3409677
DO - 10.1145/3368089.3409677
M3 - Conference contribution
AN - SCOPUS:85097175699
T3 - ESEC/FSE 2020 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering
SP - 927
EP - 938
BT - ESEC/FSE 2020 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering
A2 - Devanbu, Prem
A2 - Cohen, Myra
A2 - Zimmermann, Thomas
PB - Association for Computing Machinery
T2 - 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2020
Y2 - 8 November 2020 through 13 November 2020
ER -