106,248 research outputs found

    Baseline Assessment and Prioritization Framework for IVHM Integrity Assurance Enabling Capabilities

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    Fundamental to vehicle health management is the deployment of systems incorporating advanced technologies for predicting and detecting anomalous conditions in highly complex and integrated environments. Integrated structural integrity health monitoring, statistical algorithms for detection, estimation, prediction, and fusion, and diagnosis supporting adaptive control are examples of advanced technologies that present considerable verification and validation challenges. These systems necessitate interactions between physical and software-based systems that are highly networked with sensing and actuation subsystems, and incorporate technologies that are, in many respects, different from those employed in civil aviation today. A formidable barrier to deploying these advanced technologies in civil aviation is the lack of enabling verification and validation tools, methods, and technologies. The development of new verification and validation capabilities will not only enable the fielding of advanced vehicle health management systems, but will also provide new assurance capabilities for verification and validation of current generation aviation software which has been implicated in anomalous in-flight behavior. This paper describes the research focused on enabling capabilities for verification and validation underway within NASA s Integrated Vehicle Health Management project, discusses the state of the art of these capabilities, and includes a framework for prioritizing activities

    Development of Advanced Verification and Validation Procedures and Tools for the Certification of Learning Systems in Aerospace Applications

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    Adaptive control technologies that incorporate learning algorithms have been proposed to enable automatic flight control and vehicle recovery, autonomous flight, and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments. In order for adaptive control systems to be used in safety-critical aerospace applications, they must be proven to be highly safe and reliable. Rigorous methods for adaptive software verification and validation must be developed to ensure that control system software failures will not occur. Of central importance in this regard is the need to establish reliable methods that guarantee convergent learning, rapid convergence (learning) rate, and algorithm stability. This paper presents the major problems of adaptive control systems that use learning to improve performance. The paper then presents the major procedures and tools presently developed or currently being developed to enable the verification, validation, and ultimate certification of these adaptive control systems. These technologies include the application of automated program analysis methods, techniques to improve the learning process, analytical methods to verify stability, methods to automatically synthesize code, simulation and test methods, and tools to provide on-line software assurance

    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

    Software Testing and Metrics for Concurrent Computation

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    [[abstract]]Verification and validation are two important technologies to assure the reliability and quality of software. Software testing and metrics are two approaches to execute the verification and validation. In sequential computation, a fairly mature process exists, with various methodologies and tools available for use in building and demonstrating the correctness of a program being tested. The emergence of concurrent computation in recent years, however, introduces new testing problems and difficulties that cannot be solved by the traditional sequential program testing techniques. Many concurrent program testing methodologies have been proposed to solve controlled execution and determinism. There have been few discussions of concurrent software testing from the inter-task viewpoint, even though the common characteristics of concurrent programming are the explicit identification of the large-grain parallel computation units (tasks) and the explicit inter-task communication via a rendezvous-style mechanism. In this paper, we focus on testing concurrent programs through task decomposition. We propose four testing criteria to test a concurrent program. A programmer can choose an appropriate testing strategy depending on the properties of the concurrent programs. Associated with the strategies, four equations are provided to measure the complexity of concurrent programs[[conferencetype]]國際[[conferencedate]]19961204~19961204[[conferencelocation]]Seoul, Kore

    Verification, Analytical Validation, and Clinical Validation (V3): The Foundation of Determining Fit-for-Purpose for Biometric Monitoring Technologies (BioMeTs)

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    Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field

    Transitioning Applications to Semantic Web Services: An Automated Formal Approach

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    Semantic Web Services have been recognized as a promising technology that exhibits huge commercial potential, and attract significant attention from both industry and the research community. Despite expectations being high, the industrial take-up of Semantic Web Service technologies has been slower than expected. One of the main reasons is that many systems have been developed without considering the potential of the web in integrating services and sharing resources. Without a systematic methodology and proper tool support, the migration from legacy systems to Semantic Web Service-based systems can be a very tedious and expensive process, which carries a definite risk of failure. There is an urgent need to provide strategies which allow the migration of legacy systems to Semantic Web Services platforms, and also tools to support such a strategy. In this paper we propose a methodology for transitioning these applications to Semantic Web Services by taking the advantage of rigorous mathematical methods. Our methodology allows users to migrate their applications to Semantic Web Services platform automatically or semi-automatically

    Extensible Technology-Agnostic Runtime Verification

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    With numerous specialised technologies available to industry, it has become increasingly frequent for computer systems to be composed of heterogeneous components built over, and using, different technologies and languages. While this enables developers to use the appropriate technologies for specific contexts, it becomes more challenging to ensure the correctness of the overall system. In this paper we propose a framework to enable extensible technology agnostic runtime verification and we present an extension of polyLarva, a runtime-verification tool able to handle the monitoring of heterogeneous-component systems. The approach is then applied to a case study of a component-based artefact using different technologies, namely C and Java.Comment: In Proceedings FESCA 2013, arXiv:1302.478
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