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Automata learning algorithms and processes for providing more complete systems requirements specification by scenario generation, CSP-based syntax-oriented model construction, and R2D2C system requirements transformationSystems, methods and apparatus are provided through which in some embodiments, automata learning algorithms and techniques are implemented to generate a more complete set of scenarios for requirements based programming. More specifically, a CSP-based, syntax-oriented model construction, which requires the support of a theorem prover, is complemented by model extrapolation, via automata learning. This may support the systematic completion of the requirements, the nature of the requirement being partial, which provides focus on the most prominent scenarios. This may generalize requirement skeletons by extrapolation and may indicate by way of automatically generated traces where the requirement specification is too loose and additional information is required.
Document ID
20100017551
Acquisition Source
Headquarters
Document Type
Other - Patent
Authors
Hinchey, Michael G.
Margaria, Tiziana
Rash, James L.
Rouff, Christopher A.
Steffen, Bernard
Date Acquired
August 24, 2013
Publication Date
February 23, 2010
Subject Category
Computer Programming And Software
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
Patent
US-Patent-7,668,796
Patent Application
US-Patent-Appl-SN-11/536,132

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