3 research outputs found

    Projected Impact of Compositional Verification on Current and Future Aviation Safety Risk

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    The projected impact of compositional verification research conducted by the National Aeronautic and Space Administration System-Wide Safety and Assurance Technologies on aviation safety risk was assessed. Software and compositional verification was described. Traditional verification techniques have two major problems: testing at the prototype stage where error discovery can be quite costly and the inability to test for all potential interactions leaving some errors undetected until used by the end user. Increasingly complex and nondeterministic aviation systems are becoming too large for these tools to check and verify. Compositional verification is a "divide and conquer" solution to addressing increasingly larger and more complex systems. A review of compositional verification research being conducted by academia, industry, and Government agencies is provided. Forty-four aviation safety risks in the Biennial NextGen Safety Issues Survey were identified that could be impacted by compositional verification and grouped into five categories: automation design; system complexity; software, flight control, or equipment failure or malfunction; new technology or operations; and verification and validation. One capability, 1 research action, 5 operational improvements, and 13 enablers within the Federal Aviation Administration Joint Planning and Development Office Integrated Work Plan that could be addressed by compositional verification were identified

    Data Mining Based Decomposition for Assume-Guarantee Reasoning

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    International audienceAutomated compositional reasoning using assume-guarantee rules plays a key role in large system verification. A vexing problem is to discover fine decomposition of system contributing to appropriate assumptions. We present an automatic decomposition approach in compositional reasoning verification. The method is based on data mining algorithms. An association rule algorithm is harnessed to discover the hidden rules among system variables. A hypergraph partitioning algorithm is proposed to incorporate these rules as weight constraints for system variable clustering. The experiments demonstrate that our strategy leads to order-of-magnitude speedup over previous
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