Accurate web application performance testing relies on the use of loading tests based on a realistic client behaviour load model. Unfortunately developing such load models and associated test plans and scripts is tedious and error-prone with most existing web performance testing tools providing limited client load modelling capabilities. We describe a new approach and toolset that we have developed, MaramaMTE+, which improves the ability to model realistic web client load behaviour, automatically generates complex web application testing plans and scripts, and integrates load behaviour modelling with a generic performance engineering tool. MaramaMTE+ uses a stochastic form chart as its client loading model. A 3rd party web crawler application extracts structural information from a target web site, aggregating the collected data into a crawler database that is then used for form chart model generation. The performance engineer then augments this synthesized form chart with client loading probabilities. Realistic web loading tests for a 3rd party web load testing tool are then automatically generated from this resultant stochastic form chart client load model. We describe the development of our MaramaMTE+ environment, example usage of the tool, and compare and contrast the results obtained from our generated performance load tests against hand-built 3rd party tool load tests

Most distributed system specifications have performance benchmark requirements. However, determining the likely performance of complex distributed system architectures during development is very challenging. We describe a system where software architects sketch an outline of their proposed system architecture at a high level of abstraction, including indicating client requests, server services, and choosing particular kinds of middleware and database technologies. A fully working implementation of this system is then automatically generated, allowing multiple clients and servers to be run. Performance tests are then automatically run for this generated code and results are displayed back in the original high-level architectural diagrams. Architects may change performance parameters and architecture characteristics, comparing multiple test run results to determine the most suitable abstractions to refine to detailed designs for actual system implementation. We demonstrate the utility of this approach and the accuracy of our generated performance test-beds for validating architectural choices during early system development.