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Journal ArticleDOI

Increasing Validity of Simulation Models Through Metamorphic Testing

Megan Olsen, +1 more
- 01 Mar 2019 - 
- Vol. 68, Iss: 1, pp 91-108
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TLDR
This work proposes taking the metamorphic testing (MT) approach from the realm of software verification and applying it for validating executable simulation models, and demonstrates the successful application of MT toward validating three different simulation models: an ABM of gossip propagation, anABM of cancer, and a DES of airport check-in and security.
Abstract
Simulation validation, demonstrating that a simulation accurately represents the system it studies, poses a particularly potent form of the oracle problem. A simulation must be validated for its results to be reliable, but often no oracle exists due to the nature of simulations. We propose taking the metamorphic testing (MT) approach from the realm of software verification and applying it for validating executable simulation models. By establishing pseudo-oracles based on metamorphic relations between parameters and behaviors within an executable model, we can create a methodical approach for validating simulation models. We propose an overall framework and guidelines to apply MT for simulation validation, with details for two prevalent simulation approaches: agent-based simulation models (ABM), and discrete-event simulation (DES) modeling. Through three case studies, we demonstrate the successful application of MT toward validating three different simulation models: an ABM of gossip propagation, an ABM of cancer, and a DES of airport check-in and security.

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Citations
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METTLE: A METamorphic Testing Approach to Assessing and Validating Unsupervised Machine Learning Systems

TL;DR: In this paper, the authors developed a MET amorphic approach to assess and validate unsupervised machine learning systems, abbreviated as mettle, by explicitly considering the specific expectations and requirements of these systems from individual users' perspectives.
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METTLE: a METamorphic testing approach to assessing and validating unsupervised machine LEarning systems

TL;DR: In this article, the authors developed a metamorphic approach to assess and validate unsupervised machine learning systems, abbreviated as METTLE, by explicitly considering the specific expectations and requirements of these systems from individual users' perspectives.
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References
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