Estimating the Probability of Failure When Testing Reveals No Failures
IEEE Transactions on Software Engineering
A Markov Chain Model for Statistical Software Testing
IEEE Transactions on Software Engineering
Statistical Language Learning
Mining Access Patterns Efficiently from Web Logs
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Improved techniques for software testing based on markov chain usage models
Improved techniques for software testing based on markov chain usage models
MaTeLo - Statistical Usage Testing by Annotated Sequence Diagrams, Markov Chains and TTCN-3
QSIC '03 Proceedings of the Third International Conference on Quality Software
User-Oriented Reliability Modeling for a Web System
ISSRE '03 Proceedings of the 14th International Symposium on Software Reliability Engineering
Evaluating the markov assumption for web usage mining
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
Reliability Estimation for Statistical Usage Testing using Markov Chains
ISSRE '04 Proceedings of the 15th International Symposium on Software Reliability Engineering
Segregated failures model for availability evaluation of fault-tolerant systems
ACSC '06 Proceedings of the 29th Australasian Computer Science Conference - Volume 48
Estimating the Probability of Failure When Software Runs Are Dependent: An Empirical Study
ISSRE '09 Proceedings of the 2009 20th International Symposium on Software Reliability Engineering
Generating Transition Probabilities for Automatic Model-Based Test Generation
ICST '10 Proceedings of the 2010 Third International Conference on Software Testing, Verification and Validation
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Testing the reliability of an application usually requires a good usage model that accurately captures the likely sequences of inputs that the application will receive from the environment. The models being used in the literature are mostly based on Markov chains. They are used to generate test cases that are statistically close to what the application is expected to receive when in production. In this paper, we study the specific case of web applications. We present a model that is created directly from the log file of the application. This model is also based on Markov chains and has two components: one component, based on a modified tree, captures the most frequent behavior, while the other component is another Markov chain that captures infrequent behaviors. The result is a statistically correct model that exhibits clearly what most users do on the site. We present an experimental study on the log of a real web site and discuss strength and weakness of the model for reliability testing.