Developing a Tool to Support Synthetic Test Data Generation for Performance Testing of Application Systems
When it comes to performance testing of application systems, huge amounts of realistic test data are required in order to be able to generate the desired load level. Since the manual creation of the test data is tedious, an automatic approach is desirable.
More recently, model-based testing has become a promising approach to not only systemize but also to (partially) automate central testing activities based on information extracted from models such as UML diagrams.
Goals of the Thesis
The goal of this thesis is to develop a concept for the model-based generation of test data which meets the specific requirements of performance testing and to evaluate this concept using a prototypical implementation.
- Requirements Analysis: At the beginning, the specific requirements of test data for performance testing should be collected. In addition, the generation process and information needs of data generation tools such as Databene Benerator should be analyzed according to the test data requirements. In addition, available models and necessary manual steps should be identified.
- Conceptual Design: Based on this analysis, a concept of the prototype should be designed by relating the identified requirements and by choosing appropriate techniques of model-based testing.
- Prototypical Implementation: Finally, the concept should be realized by developing a first prototypical implementation and also by evaluating the prototype with test data for real performance tests.
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- Ayala-Rivera, V., McDonagh, P., Cerqueus, T., & Murphy, L. (2013). Synthetic Data Generation using Benerator Tool.
- Databene Benerator: http://databene.org/databene-benerator
- Databene Benerator Manual: http://databene.org/download/databene-benerator-manual-0.7.6.pdf