Communications of the ACM
ECOOP '01 Proceedings of the 15th European Conference on Object-Oriented Programming
CD '02 Proceedings of the IFIP/ACM Working Conference on Component Deployment
Racer: effective race detection using aspectj
ISSTA '08 Proceedings of the 2008 international symposium on Software testing and analysis
Incremental estimation of discrete hidden Markov models based on a new backward procedure
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
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The goal of this research is to formulate a framework to determine whether the usage of an application in production environments is consistent with the test cases used to verify it before the application was released. Aspect-Oriented Programming (AOP) techniques are used to apply the instrumentation required for the measuring process so that the program is oblivious to the instrumentation and Hidden Markov Models (HMMs) are used to create signatures of the program. This paper presents the preliminary findings on the use of such mathematical models to measure the completeness of use cases driving the quality assurance testing. To demonstrate the technique, the Web Service API of a commercial product is used. SOAP calls executed through different client applications are used to create test data for the experiments. The HMMs signatures created from collecting method calls can be used to determine whether the application is used according to the uses cases that have been verified. If the likelihood that the stochastic model obtained during testing can generate the sequences of calls collected from the production environment (via AOP techniques) is low, then it suggests that the program is being used in a way that has not been formally tested. These experiments will show that observable differences of the measurements using log likelihood graphs can detect such anomalies.