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model compared to the simulation model when the
buffer size was beyond a certain value. To mitigate
this issue, we can employ the simulation model in
solving the inversion problem. In literature, there
is an increasing interest in using simulation models
in optimization problems (Gosavi, 2014). To en-
hance the quality of the generated test cases, we pro-
posed the UEII coverage criterion. However, the net-
work model has constrained NOPs by non-linear con-
straints, making test generation using combinatorial
metrics very complicated. To overcome this issue,
we exhaustively checked all combinations for the im-
posed constraints. However, this approach might be
very expensive in terms of execution time, especially
for systems with a large number of parameters and/or
parameter values, which indicates the need for more
powerful mechanisms to address such scenarios.
REFERENCES
(2016). Advanced combinatorial testing system (acts).
Balsamo, S. et al. (2004). Model-based performance predic-
tion in software development: A survey. IEEE Trans.
on Soft. Eng., 30(5):295–310.
Bianchi, G. (2000). Performance analysis of the ieee 802.11
distributed coordination function. IEEE Journal on
Selected Areas in Communications, 18(3):535–547.
Charnes, J. M. (1995). Analyzing multivariate output. In
Proc. of the 27th conf. on Winter simulation, pages
201–208. IEEE Computer Society.
Cox, D. R. and Miller, H. D. (1977). The theory of stochas-
tic processes, volume 134. CRC Press.
Diaz, A., Merino, P., and Rivas, F. J. (2010). Mobile appli-
cation profiling for connected mobile devices. IEEE
Pervasive Computing, 9(1):54–61.
German, R. (2000). Performance analysis of communica-
tion systems with non-Markovian stochastic Petri nets.
John Wiley & Sons, Inc.
Gosavi, A. (2014). Simulation-based optimization: para-
metric optimization techniques and reinforcement
learning, volume 55. Springer.
Grindal, M., Offutt, J., and Andler, S. F. (2005). Combi-
nation testing strategies: a survey. Software Testing,
Verification and Reliability, 15(3):167–199.
Ivanovici, M. and Beuran, R. (2010). Correlating quality of
experience and quality of service for network applica-
tions. In Adibi, S., editor, Quality of service architec-
tures for wireless networks: performance metrics and
management, chapter 15, pages 326–351. IGI Global,
Pennsylvania, USA.
Jain, R. (1991). The art of computer systems performance
analysis: techniques for experimental design, mea-
surement, simulation, and modeling. John Wiley &
Sons.
Jiang, Z. and Hassan, A. (2015). A survey on load testing
of large-scale software systems. IEEE Trans. on Soft.
Eng., 41(11):1091–1118.
Joorabchi, M. E., Mesbah, A., and Kruchten, P. (2013). Real
challenges in mobile app development. In Int. Symp.
on Emp. Soft. Eng. and Meas., pages 15–24. IEEE.
Kim, Y. et al. (2013). Validating software reliability early
through statistical model checking. IEEE software,
30(3):35–41.
Koziolek, H. (2010). Performance evaluation of
component-based software systems: A survey. Per-
formance Evaluation, 67(8):634–658.
Kumar, R. et al. (2015). Inverting a steady-state. In Proc.
of the 8th Int. Conf. on Web Search and Data Mining,
pages 359–368. ACM.
Law, A. M. (2015). Simulation modeling and analysis.
McGraw-Hill, NY, fifth edition.
Li, M., Claypool, M., and Kinicki, R. (2009). Playout buffer
and rate optimization for streaming over ieee 802.11
wireless networks. ACM Trans. on Multimedia Com-
puting, Communications, and Applications, 5(3):26.
Liu, Y., Xu, C., and Cheung, S.-C. (2015). Diagnosing en-
ergy efficiency and performance for mobile internet-
ware applications. Software, IEEE, 32(1):67–75.
Matinnejad, R. et al. (2016). Automated test suite genera-
tion for time-continuous simulink models. In 38th Int.
Conf. on Soft. Eng., pages 595–606. ACM.
Mok, R. K., Chan, E. W., and Chang, R. K. (2011). Measur-
ing the quality of experience of http video streaming.
In 12th IFIP/IEEE Int. Symp. on Integrated Network
Management and Workshops, pages 485–492. IEEE.
Satoh, I. (2004). Software testing for wireless mobile com-
puting. IEEE Wireless Communications, 11(5):58–64.
Sebih, N. et al. (2014). Software model checking of udp-
based distributed applications. In 2nd Int. Symp. on
Comp. and Net., pages 96–105. IEEE.
Siavashi, F. and Truscan, D. (2015). Environment modeling
in model-based testing: concepts, prospects and re-
search challenges: a systematic literature review. In
19th Int. Conf. on Eval. and Assess. in Soft. Eng.,
page 30. ACM.
Tarantola, A. (2005). Inverse problem theory and methods
for model parameter estimation. siam.
Walls, R. J. et al. (2015). Discovering specification vio-
lations in networked software systems. In 26th Int.
Symp. on Soft. Reliab. Eng., pages 496–506. IEEE.
Yılmaz, C. et al. (2014). Moving forward with combinato-
rial interaction testing. Computer, 47(2):37–45.
A PACKET DELAY STATISTICS
IN A WIFI NETWORK
In this part, we derive analytical expressions for the
mean and variance of the packet (or frame) inter-
arrival time delay for streaming over a UDP protocol
and via a WiFi network. We assume that the WiFi
AP operates in the Distributed Coordination Function
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