![](bg9.png)
for energy profiling, code optimization and refactor-
ing of code smells/energy bugs to help aid green An-
droid development. Other types of support tools, such
as tools for interface optimization, third-party library
detection etc., were not in the scope of this study. We
plan to cover such tools in future work.
7 CONCLUSION
To get an overview of the state of the art and to find
research opportunities with respect to support tools
available for green Android development, we con-
ducted a mapping study. Based on our analysis the
current support tools were classified into three cate-
gories 1) Profiler, 2) Detector, 3) Optimizer. The main
findings of the paper are that most Profiler tools pro-
vide a graphical representation of energy consump-
tion over time. Most Detector tools provide a list
of energy bugs/code smells to be manually corrected
by a developer for the improvement of energy. Most
Optimizer automatically convert original APK/SC to
a refactored version(s) of APK/SC. The most typi-
cal technique in Detector and Optimizer category was
static source code analysis using a predefined set of
code smells and rules.
ACKNOWLEDGEMENTS
This work is supported by the Estonian Center of Ex-
cellence in ICT research (EXCITE), the group grant
PRG887 funded by the Estonian Research Council.
REFERENCES
Anwar, H. and Pfahl, D. (2017). Towards greener software
engineering using software analytics: A systematic
mapping. In Proc. of the 43rd Euromicro Conf. on
Soft. Eng. and Advanced Applications. IEEE.
Ardito, L., Procaccianti, G., Torchiano, M., and Migliore,
G. (2013). Profiling power consumption on mobile
devices. In Proc. of the 3rd Int. Conf. on Smart Grids,
Green Communications and IT Energy-aware Tech.,
pages 101–106.
Banerjee, A. and Roychoudhury, A. (2016). Auto-
mated re-factoring of android apps to enhance energy-
efficiency. In Proc. of the Int. Workshop on Mobile
Soft. Eng. and Sys. ACM Press.
Chung, Y.-F., Lin, C.-Y., and King, C.-T. (2011). ANE-
PROF: Energy profiling for android java virtual ma-
chine and applications. In Proc. of the 17th Int. Conf.
on Parallel and Distributed Sys. IEEE.
Degu, A. (2019). Android app memory and energy perfor-
mance: Systematic literature review. IOSR J. of Comp.
Eng., 21.
Fernandes, T. S., Cota, E., and Moreira, A. F. (2014). Per-
formance evaluation of android applications: A case
study. In Proc. of the Brazilian Symp. on Computing
Sys. Eng. IEEE.
Fontana, F. A., Mariani, E., Mornioli, A., Sormani, R.,
and Tonello, A. (2011). An experience report on us-
ing code smells detection tools. In Proc. of the 4th
Int. Conf. on Soft. Testing, Verification and Validation
Workshops. IEEE.
Fowler, M. (2002). Refactoring: Improving the design
of existing code. In Extreme Programming and Ag-
ile Methods — XP/Agile Universe, pages 256–256.
Springer Berlin Heidelberg.
Gartner, Inc. (2018a). Gartner says huawei secured no. 2
worldwide smartphone vendor spot, surpassing apple
in second quarter 2018. Accessed: 2019-08-30.
Gartner, Inc. (2018b). Gartner says worldwide end-user de-
vice spending set to increase 7 percent in 2018; global
device shipments are forecast to return to growth. Ac-
cessed: 2019-08-30.
GeSI (2015). Smarter2030 ict solutions for 21st century
challenges. Technical report.
Kansal, A. and Zhao, F. (2008). Fine-grained energy profil-
ing for power-aware application design. ACM Perfor-
mance Evaluation Review, 36:26–31.
Kaur, A. and Dhiman, G. (2019). A review on search-
based tools and techniques to identify bad code smells
in object-oriented systems. In Harmony Search and
Nature Inspired Optimization Algo., pages 909–921.
Springer Singapore.
Li, L., Bissyand
´
e, T. F., Papadakis, M., Rasthofer, S., Bar-
tel, A., Octeau, D., Klein, J., and Traon, L. (2017).
Static analysis of android apps: A systematic litera-
ture review. Info. and Soft. Tech., 88:67–95.
Manotas, I., Bird, C., Zhang, R., Shepherd, D., Jaspan, C.,
Sadowski, C., Pollock, L., and Clause, J. (2016). An
empirical study of practitioners’ perspectives on green
software engineering. In Proc. of the 38th Int. Conf.
on Soft. Eng. ACM Press.
Mindsea (2019). 25 Mobile App Usage Statistics To Know
In 2019. Accessed: 2019-08-05.
Pathak, A., Hu, Y. C., and Zhang, M. (2011). Bootstrap-
ping energy debugging on smartphones. In Proc. of
the 10th ACM Workshop on Hot Topics in Networks.
ACM Press.
Pathak, A., Hu, Y. C., and Zhang, M. (2012). Where is the
energy spent inside my app? In Proc. of the 7th ACM
european conf. on Computer Sys. ACM Press.
Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M.
(2008). Systematic mapping studies in soft. eng. BCS
Learning & Development.
Powell, J. (2019). Scientists reach 100 Bulletin of Science,
Tech. and Society, 37:183–184.
Singh, S. and Kaur, S. (2018). A systematic literature re-
view: Refactoring for disclosing code smells in ob-
ject oriented software. Ain Shams Eng. J., 9(4):2129–
2151.
Tool Support for Green Android Development: A Systematic Mapping Study
417