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Title: A data mining approach to discover temporal relationship among performance metrics from automated testing
Authors: Lee, EMH
Chan, KCC 
Issue Date: 2005
Source: Proceedings of the Ninth IASTED International Conference on Software Engineering and Applications (SEA 2005), Phoenix, AZ., USA, 14-16 November, p. 19-27
Abstract: Companies seek to ensure software speed and scalability by testing the performance of their systems, generating large volumes of performance data that is presented graphically and analyzed by testers. This is a time consuming process. This paper proposes a way to process these large amounts of data using a machine learning technique from association rule mining. It suggests an improvement to the current automated test tool model, a data-driven model which can be used to automatically find useful knowledge in target data. Domain experts, in this case software testers, can use this knowledge as a reference in data analysis. We experiment this technique with the load-testing results captured in the evaluation of an open source phone directory application by a company. The test results are pre-processed and input into a data mining engine. The analytical method is fast, accurate and discovers interesting rules. We discuss how these results might be used in a practical setting.
Keywords: Automated software engineering
Performance testing
Performance metrics
Temporal data mining
Association rule mining
And multivariate time series analysis
Appears in Collections:Conference Paper

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