Predictions from simulations have entered the mainstream of public policy and decision-making practices. Unfortunately, methods for gaining insight into faulty simulations outputs have not kept pace. Ideally, an insight gathering method would automatically identify the cause of a faulty output and explain to the simulation developer how to correct it. In the field of software engineering, this challenge has been addressed for general-purpose software through statistical debuggers. We present two research contributions, elastic predicates and many-valued labeling functions, that enable debuggers designed for general-purpose software to become more effective for simulations employing random variates and continuous numbers. Elastic predicates address deficiencies of existing debuggers related to continuous numbers, whereas many-valued labeling functions support the use of random variates.Recommended citation: Gore, Ross; Reynolds Jr, Paul F; Kamensky, David; Diallo, Saikou; Padilla, Jose. (2015). "Statistical debugging for simulations". ACM Transactions on Modeling and Computer Simulation (TOMACS). 25(3), 1-26.
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