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Understand tool. The plugin is used to gather the re-
sults for the evaluation, which involved applying the
metrics to one of the SAS exemplars.
In regards to future work, combining the ap-
proaches of static and dynamic code analysis could
lead to a more fruitful space in which to design met-
rics. Tackling the RQ with hybrid code analysis, for
example, could accomplish this. In addition to this,
further developing and implementing the metrics that
are based on dynamic code analysis would make the
suite more comprehensive and complete.
REFERENCES
Cheng, B. H. C., de Lemos, R., Giese, H., Inverardi, P.,
Magee, J., Andersson, J., Becker, B., Bencomo, N.,
Brun, Y., Cukic, B., Serugendo, G. D. M., Dustdar, S.,
Finkelstein, A., Gacek, C., Geihs, K., Grassi, V., Kar-
sai, G., Kienle, H. M., Kramer, J., Litoiu, M., Malek,
S., Mirandola, R., M
¨
uller, H. A., Park, S., Shaw, M.,
Tichy, M., Tivoli, M., Weyns, D., and Whittle, J.
(2009). Software engineering for self-adaptive sys-
tems: A research roadmap. In Software Engineering
for Self-Adaptive Systems, LNCS 5525, pages 1–26.
Garlan, D., Schmerl, B., and Cheng, S.-W. (2009). Soft-
ware Architecture-Based Self-Adaptation, pages 31–
55. Springer.
Gerostathopoulos, I. and Pournaras, E. (2019). Trapped in
traffic? a self-adaptive framework for decentralized
traffic optimization. In Intl Symposium on Sw Eng for
Adaptive and Self-Managing Systems, pages 32–38.
Gerostathopoulos, I., Raibulet, C., and Alberts, E. (2022).
Assessing self-adaptation strategies using cost-benefit
analysis. In 19th Intl Conference on Software Archi-
tecture Companion (ICSA-C), pages 92–95.
Gomes, I. V., Morgado, P., Gomes, T., and Moreira, R. M.
L. M. (2009). An overview on the static code analysis
approach in software development.
Hande, N. and Rao, P. V. (2017). A comparative study of
static, dynamic and hybrid analysis techniques for an-
droid malware detection. International Journal of En-
gineering Development and Research, 5:1433–1436.
Kaddoum, E., Raibulet, C., Georg
´
e, J.-P., Picard, G., and
Gleizes, M.-P. (2010). Criteria for the evaluation of
self-* systems. In ICSE Workshop on Software Eng
for Adaptive and Self-Managing Systems, page 29–38.
Kritzinger, D. (2017). 9 - development assurance. In
Kritzinger, D., editor, Aircraft System Safety, pages
193–324. Woodhead Publishing.
Krupitzer, C., Roth, F. M., VanSyckel, S., Schiele, G.,
and Becker, C. (2015). A survey on engineering ap-
proaches for self-adaptive systems. Pervasive Mob.
Comput., 17:184–206.
Kukreja, N. (2015). Measuring software maintainability,
https://quandarypeak.com/2015/02/measuring-
software-maintainability/.
Maia, P., Vieira, L., Chagas, M., Yu, Y., Zisman, A., and
Nuseibeh, B. (2019). Dragonfly: a tool for simulat-
ing self-adaptive drone behaviours. In Intl Sympo-
sium on Software Engineering for Adaptive and Self-
Managing Systems (SEAMS).
Masciadri, L. and Raibulet, C. (2009). Frameworks for
the development of adaptive systems: Evaluation of
their adaptability feature through software metrics. In
4th International Conf on Software Engineering Ad-
vances, pages 309–312. IEEE Computer Society.
McCabe, T. (1976). A complexity measure. IEEE Transac-
tions on Software Engineering, SE-2(4):308–320.
Perez-Palacin, D., Mirandola, R., and Merseguer, J. (2014).
On the relationships between qos and software adapt-
ability at the architectural level. Journal of System and
Software, 87:1–17.
Pollack, S. V. and Cytron, R. K. (2003). Executable State-
ment, page 683–684. John Wiley and Sons Ltd., GBR.
Raibulet, C. (2018). Towards a taxonomy for the evaluation
of self-* software. In 3rd Intl Workshops on Founda-
tions and Applications of Self* Systems, pages 22–23.
Raibulet, C., Arcelli Fontana, F., Capilla, R., and Carrillo,
C. (2017). Chapter 13 - an overview on quality evalu-
ation of self-adaptive systems. pages 325–352.
Raibulet, C., Arcelli Fontana, F., and Carettoni, S. (2020). A
preliminary analysis of self-adaptive systems accord-
ing to different issues. In Software Quality Journal,
volume 28, pages 1213–1243.
Raibulet, C. and Fontana, F. A. (2017). Evaluation of self-
adaptive systems: a women perspective. In 11th Euro-
pean Conference on Software Architecture, pages 23–
30.
Rawat, M. S., Mittal, A., and Dubey, S. K. (2012). Sur-
vey on impact of software metrics on software quality.
Journal of Advanced CS and Applications, 3(1).
Reinecke, P., Wolter, K., and van Moorsel, A. (2010). Eval-
uating the adaptivity of computing systems. Perfor-
mance Evaluation Journal, 67(8):676–693.
Salehie, M. and Tahvildari, L. (2009). Self-adaptive soft-
ware: Landscape and research challenges. Trans on
Autonomous and Adaptive Systems, 4(2):14:1–14:42.
Turetken, O. (2013). Towards a maintainability model for
business processes: Adapting a software maintainabil-
ity model. In Workshop on Communicat., pages 1–4.
Weyns, D. and Iftikhar, M. U. (2019). Activforms: A
model-based approach to engineer self-adaptive sys-
tems. ACM TOSEM, 10(10):1–59.
Weyns, D., Schmerl, B. R., Grassi, V., Malek, S., Mi-
randola, R., Prehofer, C., Wuttke, J., Andersson, J.,
Giese, H., and G
¨
oschka, K. M. (2010). On patterns for
decentralized control in self-adaptive systems. In Soft-
ware Engineering for Self-Adaptive Systems, pages
76–107.
Wuttke, J., Brun, Y., Gorla, A., and Ramaswamy, J. (2012).
Traffic routing for evaluating self-adaptation. In Intl
Symposium on Software Engineering for Adaptive and
Self-Managing Systems, pages 27–32.
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