Invention Grant
US07668796B2 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 transformation
有权
自动机学习算法和过程,通过场景生成,基于CSP的语法导向模型构建和R2D2C系统需求转换来提供更完整的系统需求规范
- Patent Title: 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 transformation
- Patent Title (中): 自动机学习算法和过程,通过场景生成,基于CSP的语法导向模型构建和R2D2C系统需求转换来提供更完整的系统需求规范
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Application No.: US11536132Application Date: 2006-09-28
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Publication No.: US07668796B2Publication Date: 2010-02-23
- Inventor: Michael G. Hinchey , Tiziana Margaria , James L. Rash , Christopher A. Rouff , Bernard Steffen
- Applicant: Michael G. Hinchey , Tiziana Margaria , James L. Rash , Christopher A. Rouff , Bernard Steffen
- Applicant Address: US DC Washington
- Assignee: The United States of America as represented by the Administrator of the National Aeronautics and Space Administration
- Current Assignee: The United States of America as represented by the Administrator of the National Aeronautics and Space Administration
- Current Assignee Address: US DC Washington
- Agent Heather Goo
- Main IPC: G06N5/02
- IPC: G06N5/02 ; G06N3/08

Abstract:
Systems, 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.
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