Proceedings of the 4th International Conference on Quality Software
(QSIC '04),
IEEE Computer Society Press, Los Alamitos, CA, pp. 32-40 (2004) |
M.Y. Cheng 2 , S.C. Cheung 3 , and T.H. Tse 4
[paper from IEEE Xplore | paper from IEEE digital library | technical report TR-2004-05]
ABSTRACT |
The advances in computer and graphic technologies have led
to the popular use of multimedia for information exchange.
However, multimedia systems are difficult to test.
A major reason is that these systems generally exhibit fuzziness
in their temporal behaviors.
The fuzziness may be caused by the existence of non-deterministic factors
in their runtime environments, such as system load and network traffic.
It complicates the analysis of test results.
The problem is aggravated when a test involves the synchronization of
different multimedia streams as well as variations in system loading.
In this paper, we conduct an empirical study on the testing and fault-identification of multimedia systems by treating the issue as a classification problem. Typical classification techniques, including Bayesian networks, k-nearest neighbor, and neural networks, are experimented with the use of X-Smiles, an open sourced multimedia authoring tool supporting the Synchronized Multimedia Integration Language (SMIL). From these experiments, we make a few interesting observations and give plausible explanations based on the geometrical properties of the test results. Keywords: Software testing, multimedia, classification, Bayesian networks, k-nearest neighbor, neural networks |
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