Testing Monte Carlo Algorithmic Systems
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Summary:
This article covers the unique challenge in defining testing scope and expected results when testing systems with non-deterministic outputs whose accuracy improves over repeated iterations of the same inputs. A thorough understanding of the algorithms under test and excellent communication between development and testing are essential in test scenario definition and predicting anticipated outcomes. Defining tests and expected behaviors prior to the start of testing is especially crucial in these types of conditions.
About the author
Frank Erdman is a Software QA Engineer in Austin, Texas. His background includes testing mobile workforce management systems which use stochastic algorithms for mobile resource planning, forecasting, and scheduling. Unit test and automation tools he has worked with include JUnit, XMLUnit, CPPUnit, and Borland SilkTest. He is COMPTIA A+ and Network+ certified, and was a website consultant for Calliope, LLC, a talent agency in San Antonio. You can reach Frank via e-mail at [email protected] or his blog http://blogkinnetic.blogspot.com.
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