Efficient Population of the Verification Tradespace Using Bayesian Inference

Farshad Farkhondehmaal, Alejandro Salado

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


This article presents a process to efficiently elicit belief models of verification strategies in the context of applying tradespace exploration to the design of verification strategies. The need for efficiency is at the core of tradespace exploration, where a large region of the solution space needs to be explored before anchoring to a specific solution or solution set. In conceptual design and systems architecture, solutions are modeled by leveraging independent models that are connected by an overarching model. In designing verification strategies, however, verification activities cannot be modeled independently of the overall strategy in which they are used. As a result, probabilities associated with the confidence generated by each verification activity, need to be elicited for each candidate verification strategy individually. This requires, in principle, to generate a specific model for each solution in the solution space, which may hamper efficiency. In this article, we show that Bayesian inference can be used to overcome this limitation. In particular, the proposed process in this article uses only one elicitation activity for an overarching verification strategy and then leverages Bayesian inference to determine automatically the probabilities of other verification strategies in the solution space.

Original languageEnglish (US)
Article number8809385
Pages (from-to)3225-3232
Number of pages8
JournalIEEE Systems Journal
Issue number3
StatePublished - Sep 2020
Externally publishedYes


  • Bayesian Inference
  • system verification and validation
  • tradespace exploration

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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