Software testing techniques (2nd ed.)
Software testing techniques (2nd ed.)
Communication and computing for distributed multimedia systems
Communication and computing for distributed multimedia systems
Maintaining knowledge about temporal intervals
Communications of the ACM
Machine Learning
Constructing Bayesian-network models of software testing and maintenance uncertainties
ICSM '97 Proceedings of the International Conference on Software Maintenance
Toward Generic Timing Tests for Distributed Multimedia Software Systems
ISSRE '01 Proceedings of the 12th International Symposium on Software Reliability Engineering
Using Machine Learning for Estimating the Defect Content After an Inspection
IEEE Transactions on Software Engineering
A media synchronization survey: reference model, specification, and case studies
IEEE Journal on Selected Areas in Communications
Journal of Systems and Software - Special issue: Quality software
Distributed computation of the knn graph for large high-dimensional point sets
Journal of Parallel and Distributed Computing
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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 is 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 source multimedia authoring tool supporting the Synchronized Multimedia Integration Language (SMIL). The encouraging result of our study, which is based only on five attributes, shows that our proposal can achieve an accuracy of 57.6 to 79.2% in identifying the types of fault in environments where common cause variations are present. A further improvement of 7.6% is obtained via normalization.