Proceedings of the 4th International Conference on Quality Software (QSIC '04),
IEEE Computer Society Press, Los Alamitos, CA, pp. 32-40 (2004)

Towards the Application of Classification Techniques
to Test and Identify Faults in Multimedia Systems

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]


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

1. This research is supported in part by a grant of the Research Grants Council of Hong Kong and a grant of The University of Hong Kong.
2. Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong.
3. Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, NT, Hong Kong.
4. (Corresponding author.)
Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong.


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