Ensuring completeness of formal verification with Gap Free Verification.
Abstract not provided.
Punnoose, Ratish J.↗
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Electropotential Verification for Nuclear Facility Design Information Verification
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Baldez, Phoenix↗
Computer Simulations to Study Diffraction Effects of Stacking Faults in Beta-SiC: II. Experimental Verification: Experimental Verification - 2
Earlier results from computer simulation studies suggest a correlation between the spatial distribution of stacking errors in the Beta-SiC structure and features observed in X-ray diffraction patterns of the material. Reported here are experimental results obtained from two types of nominally Beta-SiC specimens, which yield distinct XRD data. These samples were analyzed using high resolution transmission electron microscopy (HRTEM) and the stacking error distribution was directly determined. The HRTEM results compare well to those deduced by matching the XRD data with simulated spectra, confirming the hypothesis that the XRD data is indicative not only of the presence and density of stacking errors, but also that it can yield information regarding their distribution. In addition, the stacking error population in both specimens is related to their synthesis conditions and it appears that it is similar to the relation developed by others to explain the formation of the corresponding polytypes.
Pujar, Vijay V.↗
Electropotential Verification for Nuclear Facility Design Information Verification
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Baldez, Phoenix↗
An Evaluation of Extended Reality Technologies for Use in Verification Testing at NASA 2024 HRP IWS Abstract
BACKGROUND At NASA, verification testing is the formal process of ensuring that a product conforms to requirements set by a project or program. Some verification methods, such as Demonstrations and Test, require either the end product or a mockup of the product with sufficient fidelity to stand-in for the product during the test. Traditionally, these mockups have been physical (e.g., foam-core and wood) but there is growing interest in exploring new methods for testing with these mockups. These methods include virtual reality (VR), mixed reality, and augmented reality which are collectively referred to as eXtended Reality (XR) technologies. VR has already been adopted and used by many in the aerospace industry as a tool for use in early design phases (e.g., developmental testing) and may have the most potential for use in verification tests. Benefits of using VR mockups offer cost effectiveness, ease of iteration, simulation of hazardous conditions (e.g., an egress through a hatch with smoke obscuring vision), and the ability to simulate microgravity conditions, which are challenging to do with physical mockups. However, the validity of test results obtained from VR mockup demonstrations or testing, compared to the current gold standard of physical mockups, remains uncertain. It is unlikely that there is one clean answer as there are many different types of verification outcomes and each XR technology must be evaluated on its own merits. This is not an issue during developmental testing as the design is still in flux and the total success of the design is not dependent upon the results of a developmental test. Verification tests, however, only happen once, assuming no change to the design, and the results are used to certify the product. Therefore, establishing the validity of XR mockup-based verification outcomes is essential before considering them for any use in verification tests. OBJECTIVE AND METHOD To address this concern, the Human Research Program has funded a project to explore and qualify how XR technologies might be used in verification demonstration and testing at NASA. Currently, we are conducting a review of the literature on the utilization of XR mockups for design activities, prototyping, and user testing. We are employing the Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis method to identify the pros, cons, and barriers to adoption of XR technologies for verification testing at NASA. Additionally, we are developing a framework to guide the deployment of XR mockups for verification tests. Building upon available evidence from the literature and subject-matter expert feedback, our goal for the framework is to provide guidelines for which forms of XR mockups are suitable for a given verification test, when only physical mockups should be employed and to highlight areas for which more evidence is needed. To further refine our framework and to contribute to the body of evidence, we are planning a lab-based experiment comparing a VR mockup to a physical twin for a set of select verification outcomes. ANTICIPATED RESULTS In this presentation, we will present the work we conducted to evaluate XR technologies for use in verification tests at NASA. We will summarize and report our findings from the SWOT analysis and our lab-based study, and we will present the current state of the XR Technologies for Verification Testing framework. We will conclude by summarizing remaining work and future directions for the project. Technologies for Verification Testing framework. We will conclude by summarizing remaining work and future directions for the project.
Extended Reality↗
Regression Verification Using Impact Summaries
Regression verification techniques are used to prove equivalence of syntactically similar programs. Checking equivalence of large programs, however, can be computationally expensive. Existing regression verification techniques rely on abstraction and decomposition techniques to reduce the computational effort of checking equivalence of the entire program. These techniques are sound but not complete. In this work, we propose a novel approach to improve scalability of regression verification by classifying the program behaviors generated during symbolic execution as either impacted or unimpacted. Our technique uses a combination of static analysis and symbolic execution to generate summaries of impacted program behaviors. The impact summaries are then checked for equivalence using an o-the-shelf decision procedure. We prove that our approach is both sound and complete for sequential programs, with respect to the depth bound of symbolic execution. Our evaluation on a set of sequential C artifacts shows that reducing the size of the summaries can help reduce the cost of software equivalence checking. Various reduction, abstraction, and compositional techniques have been developed to help scale software verification techniques to industrial-sized systems. Although such techniques have greatly increased the size and complexity of systems that can be checked, analysis of large software systems remains costly. Regression analysis techniques, e.g., regression testing [16], regression model checking [22], and regression verification [19], restrict the scope of the analysis by leveraging the differences between program versions. These techniques are based on the idea that if code is checked early in development, then subsequent versions can be checked against a prior (checked) version, leveraging the results of the previous analysis to reduce analysis cost of the current version. Regression verification addresses the problem of proving equivalence of closely related program versions [19]. These techniques compare two programs with a large degree of syntactic similarity to prove that portions of one program version are equivalent to the other. Regression verification can be used for guaranteeing backward compatibility, and for showing behavioral equivalence in programs with syntactic differences, e.g., when a program is refactored to improve its performance, maintainability, or readability. Existing regression verification techniques leverage similarities between program versions by using abstraction and decomposition techniques to improve scalability of the analysis [10, 12, 19]. The abstractions and decomposition in the these techniques, e.g., summaries of unchanged code [12] or semantically equivalent methods [19], compute an over-approximation of the program behaviors. The equivalence checking results of these techniques are sound but not complete-they may characterize programs as not functionally equivalent when, in fact, they are equivalent. In this work we describe a novel approach that leverages the impact of the differences between two programs for scaling regression verification. We partition program behaviors of each version into (a) behaviors impacted by the changes and (b) behaviors not impacted (unimpacted) by the changes. Only the impacted program behaviors are used during equivalence checking. We then prove that checking equivalence of the impacted program behaviors is equivalent to checking equivalence of all program behaviors for a given depth bound. In this work we use symbolic execution to generate the program behaviors and leverage control- and data-dependence information to facilitate the partitioning of program behaviors. The impacted program behaviors are termed as impact summaries. The dependence analyses that facilitate the generation of the impact summaries, we believe, could be used in conjunction with other abstraction and decomposition based approaches, [10, 12], as a complementary reduction technique. An evaluation of our regression verification technique shows that our approach is capable of leveraging similarities between program versions to reduce the size of the queries and the time required to check for logical equivalence. The main contributions of this work are: - A regression verification technique to generate impact summaries that can be checked for functional equivalence using an off-the-shelf decision procedure. - A proof that our approach is sound and complete with respect to the depth bound of symbolic execution. - An implementation of our technique using the LLVMcompiler infrastructure, the klee Symbolic Virtual Machine [4], and a variety of Satisfiability Modulo Theory (SMT) solvers, e.g., STP [7] and Z3 [6]. - An empirical evaluation on a set of C artifacts which shows that the use of impact summaries can reduce the cost of regression verification.
Backes, John↗
Guiding Integration of Formal Verification in Assurance Cases
Assurance cases are being increasingly acknowledged as away to build trust in complex systems with autonomous capabilities. An assurance case is a comprehensive, defensible, and valid justification that a system will function as intended for a specific mission and operating environment. Formal verification is often reserved for the most critical components of such systems. However, formal verification tools are often complex, and their usage is subject to many constraints and contextual dependencies. This can raise challenges both for performing the verification as well as reflecting the verification results appropriately in the assurance case, especially for non-expert users of the verification tool. To address these challenges, we present a tool-supported methodology for integrating formal verification results in an assurance case by capturing key verification method information in a rigorously constructed assurance case. In particular, we capture the tool specification in terms of its inputs, outputs, and assurance constraints as assumptions over inputs and guarantees provided over its outputs. The tool specification is parametrized over the inputs and outputs to both guide the intended application of the tool, as well as to check that the tool has been applied following the stated assumptions and that the guarantees hold. We define a generic tool assurance argument pattern that enables integration of the verification results in the assurance case by allowing custom refinement and automated instantiation for each tool use. We demonstrate our methodology on two formal verification tools and their applications to the verification of neural network properties for the aircraft domain.
Assurance Cases↗
Structural Verification of the Redesigned Space Shuttle Bipod Foam Closeout
This document outlines the structural verification approach for the Space Shuttle External Tank Forward Bipod Foam Closeout. Due to the Space Shuttle Columbia accident, debris has become a major concern. The intent of the structural verification is to ensure that any debris shed from the bipod is within acceptable limits. Since cohesive failure due to internal defects was identified as the most likely cause of the STS-107 bipod ramp foam failure, verification for this failure mode receives particular emphasis. However, all failure modes for TPS are considered and appropriate verification rationale is developed for each failure mode. Figure 1 depicts the structural verification of a production design where analysis and test are the primary methods of verification. It can be seen that the successful completion of structural verification is dependent on three main areas: 1. Production process control and quality assurance must ensure that test articles and/or analytical models are representative of (or conservatively envelope) production hardware in terms of geometry, materials and processing. Variability and defects must be considered. 2. Flight environments must be sufficiently characterized to bound driving environments for all failure modes. Applied environments, either test or analytical, must be representative of flight environments and have a load factor that satisfies design requirements. 3. Structural verification must include all failure modes. A comprehensive list of failure modes and the underlying failure mechanisms has been generated based on flight and test experience. Verification tests and / or analyses must address each failure mode. ET TPS Verification is accomplished by a combination of analysis, test, and similarity.
Poole, Eric L.↗
The SeaHorn Verification Framework
In this paper, we present SeaHorn, a software verification framework. The key distinguishing feature of SeaHorn is its modular design that separates the concerns of the syntax of the programming language, its operational semantics, and the verification semantics. SeaHorn encompasses several novelties: it (a) encodes verification conditions using an efficient yet precise inter-procedural technique, (b) provides flexibility in the verification semantics to allow different levels of precision, (c) leverages the state-of-the-art in software model checking and abstract interpretation for verification, and (d) uses Horn-clauses as an intermediate language to represent verification conditions which simplifies interfacing with multiple verification tools based on Horn-clauses. SeaHorn provides users with a powerful verification tool and researchers with an extensible and customizable framework for experimenting with new software verification techniques. The effectiveness and scalability of SeaHorn are demonstrated by an extensive experimental evaluation using benchmarks from SV-COMP 2015 and real avionics code.
Model Checking↗
An Analysis of Extended Reality Mockups for Use in Verification: Phase 2
NASA commercial providers are increasing their use of Virtual Reality (VR) and Hybrid Reality (HR) technologies in their system development process. The use of these technologies, collectively referred to as Extended Reality (XR) technologies, has been limited to the development phase, but there have been requests to integrate VR and HR mockups into verifications. Verifications require, at minimum, a high-fidelity mockup for activities involving human participation (tests, demonstrations, inspections). HR technology shares many of the strengths but may not share some of the critical shortcomings of VR. Specifically, HR allows for integrating physical objects, such as a suit, which may be critical to evaluating a system. Adopting HR technology still has many of the same challenges as VR. Namely, there is little to no data about the validity of evaluations conducted with HR mockups, an established process, or criteria for evaluating the appropriateness of HR mockups. Because verifications are final and only need to happen once, HR mockups must be adequately vetted before being approved for use in verifications. Further hindering the adoption of these technologies is the lack of consensus on what constitutes a high-fidelity XR mockup. Even for physical mockups, there are guidelines and common criteria, but nothing has been formalized. Discussion about when an XR mockup can be used would be greatly helped by clearly defining what a high-fidelity XR mockup is and how it might be measured. The team will engage with relevant stakeholders to learn more about the benefits and challenges of adopting hybrid reality mockups for verification. The team will build upon work from Phase 1 by maturing a framework to guide decisions for evaluating XR mockups for use in verifications. The team will also conduct experimental studies to evaluate advantages and tradeoffs of hybrid/mixed reality relative to physical and virtual reality mockups. Finally, data from Phase 1 and Phase 2 will be synthesized into a set of best practices for developing and validating XR mockups for verification. We will highlight the work conducted to date in Phase 2. We will present the status of the XR for Verification Framework, a summary of best practices identified thus far, and give a synopsis of future work.
verification testing↗
Verification of RESRAD-OFFSITE Code (V.4)
This report documents the verification of RESRAD-OFFSITE Version 4.0 and describes, where necessary, the verification of the following: • The data comprising the standard dose and risk coefficient libraries in the RESRAD database files Master_dcf_ICRP07.mdb and Master_dcf_2k.mdb. • The extraction and transfer of the data from the selected database file to the computational code by the RESRAD-OFFSITE 4.0 interface, ResOWin.exe. • The different processes that are modeled by the main computational code in RESRAD OFFSITE 4.0, ResOMain.exe. • The data displayed in the graphical and text reports. Many verifications were performed as part of the quality assurance quality control program associated with the development and release of RESRAD-OFFSITE 4.0, namely: • developer testing, • internal independent testing, and • release testing. Some were also performed in response to questions from users regarding the performance of the code. The main text of the report focuses on summarizing a subset of those tests, both independent and developer tests that verified the computations performed by the code. The verifications included in this report served as the basis for the development of the release tests of the computational executables and provided the quantitative results to be compared with the code output. The input and output interfaces and the data transfers between the various executables of the code were tested while performing the verification testing. They were tested intentionally during release testing. This report also provides some basic information to help in understanding the activities that were verified. The report: • outlines the components of RESRAD-OFFSITE 4.0 and the interconnections between these components, • outlines the processes modeled by the computational code, • provides summary figures and tables to offer confirmation of the verification of the computational components of the code, • reproduces the verifiers’ reports, if available, in individual appendices, • refers to the previous verification report (Yu et al. 2011) for more details about some of the verifications, and • reproduces the test cases and the testers’ reports from the release testing in individual appendices, when possible.
54 ENVIRONMENTAL SCIENCES↗
HDL to verification logic translator
The increasingly higher number of transistors possible in VLSI circuits compounds the difficulty in insuring correct designs. As the number of possible test cases required to exhaustively simulate a circuit design explodes, a better method is required to confirm the absence of design faults. Formal verification methods provide a way to prove, using logic, that a circuit structure correctly implements its specification. Before verification is accepted by VLSI design engineers, the stand alone verification tools that are in use in the research community must be integrated with the CAD tools used by the designers. One problem facing the acceptance of formal verification into circuit design methodology is that the structural circuit descriptions used by the designers are not appropriate for verification work and those required for verification lack some of the features needed for design. We offer a solution to this dilemma: an automatic translation from the designers' HDL models into definitions for the higher-ordered logic (HOL) verification system. The translated definitions become the low level basis of circuit verification which in turn increases the designer's confidence in the correctness of higher level behavioral models.
Gambles, J. W.↗
Requirement Assurance: A Verification Process
Requirement Assurance is an act of requirement verification which assures the stakeholder or customer that a product requirement has produced its "as realized product" and has been verified with conclusive evidence. Product requirement verification answers the question, "did the product meet the stated specification, performance, or design documentation?". In order to ensure the system was built correctly, the practicing system engineer must verify each product requirement using verification methods of inspection, analysis, demonstration, or test. The products of these methods are the "verification artifacts" or "closure artifacts" which are the objective evidence needed to prove the product requirements meet the verification success criteria. Institutional direction is given to the System Engineer in NPR 7123.1A NASA Systems Engineering Processes and Requirements with regards to the requirement verification process. In response, the verification methodology offered in this report meets both the institutional process and requirement verification best practices.
Alexander, Michael G.↗
Projected Impact of Compositional Verification on Current and Future Aviation Safety Risk
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.
Reveley, Mary S.↗
Guidelines for Verification Strategies to Minimize RISK Based on Mission Environment, -Application and -Lifetime (MEAL)
There is a trend of compromising verification testing to address the cost and schedule constraints, which poses a high-risk posture for programs/projects. Current and emerging aerospace scientific and/or human exploration programs continue to pose new technological challenges. These technological challenges combined with finite budgets and truncated schedules are forcing designers, scientists, engineers, and managers to push technologies to their physical limits. In addition, budget and schedule pressures challenge how those technologies/missions are verified. A clear understanding of the different verification processes is needed to ensure the proper verification of the technology within the mission (i.e., capabilities, advantages, and limitations). The goal of verification is to prove through test, analysis, inspection, and/or demonstration that a product provides its required function while meeting the performance requirements. It is important that verification yield understanding of representative performance under worst-case conditions so that margins to failure can be evaluated for proposed applications. The capabilities, advantages, and limitations of the testing and inspection performed at each level are different, and the risk incurred by omitting a verification step depends on the level of integration as well as Mission, Environment, Application and Lifetime (MEAL). This paper focuses on verification processes. The goal of the verification process is to ensure the given avionics technology could be safely implemented on the given MEAL consistent with the program/project risk posture.
Gonzalez, Oscar↗
Verification of Maximal Oxygen Uptake in Active Military Personnel During Treadmill Running
Figueiredo, PS, Looney, DP, Pryor, JL, Doughty, EM, McClung, HL, Vangala, SV, Santee, WR, Beidleman, BA, and Potter, AW. Verification of maximal oxygen uptake in active military personnel during treadmill running. J Strength Cond Res 36(4): 1053–1058, 2022—It is unclear whether verification tests are required to confirm “true” maximal oxygen uptake ($\dot{V}_{o2}$max) in modern warfighter populations. Our study investigated the prevalence of $\dot{V}_{o2}$max attainment in U.S. Army soldiers performing a traditional incremental running test. In addition, we examined the utility of supramaximal verification testing as well as repeated trials for familiarization for accurate $\dot{V}_{o2}$max assessment. Sixteen U.S. Army soldiers (1 woman, 15men; age, 21±2 years; height, 1.73±0.06m; body mass, 71.6±10.1 kg) completed 2 laboratory visits, each with an incremental running test (modified Astrand protocol) and a verification test (110% maximal incremental test speed) on a motorized treadmill. We evaluated $\dot{V}_{o2}$max attainment during incremental testing by testing for the definitive $\dot{V}_{o2}$ plateau using a linear least-squares regression approach. Peak oxygen uptake ($\dot{V}_{o2}$peak) was considered statistically equivalent between tests if the 90% confidence interval around the mean difference was within 62.1 ml·kg -1 ·min -1 . Oxygen uptake plateaus were identified in 14 of 16 volunteers for visit 1 (87.5%) and all 16 volunteers for visit 2 (100%). Peak oxygen uptake was not statistically equivalent, apparent from the mean difference in $\dot{V}_{o2}$peak measures between the incremental test and verification test on visit 1 (2.3 ml·kg -1 ·min -1 , [1.3–3.2]) or visit 2 (1.1 ml·kg -1 ·min -1 [0.2–2.1]). Interestingly, $\dot{V}_{o2}$peak was equivalent, apparent from the mean difference in $\dot{V}_{o2}$peak measures between visits for the incremental tests (0.0ml·kg -1 ·min -1 [-0.8to 0.9]) but not the verification tests (-1.2 ml·kg -1 ·min -1 [-2.2 to -0.2]). Modern U.S. Army soldiers can attain $\dot{V}_{o2}$max by performing a modified Astrand treadmill running test. Additional familiarization and verification tests for confirming $\dot{V}_{o2}$max in healthy active military personnel may be unnecessary.
60 APPLIED LIFE SCIENCES↗
Verification of the DIF3D Software to Support Fast Reactor Analysis
Ongoing design activities at Argonne National Laboratory are requiring a thorough verification of the Argonne Reactor Computation codes be performed. DIF3D is central to this system. The driver for this effort requires the 3D Cartesian, triangular-Z, and hexagonal-Z core geometry options of DIF3D be verified. Previous work identified the DIF3D features required to be verified to support current design activities, features of which are generally applicable to hexagonal-Z fast reactor designs. The scope of this verification effort includes verifying DIF3D’s ability to correctly translate the user’s model in to DIF3D’s preferred format, verifying that options planned for use have the desired effect, and verifying the correctness of the eigenvalue, fixed-source, forward, and adjoint solvers in DIF3D-FD and DIF3D-VARIANT. This manuscript provides the verification tasks and their results with respect to the features needed for current design activities. Since analytic solutions of the neutron diffusion and transport equations are either limited in scope or not possible, multiple tiers of problems unique to each solver and geometry type were implemented. Each of these tiers tests features independent and complementary arguments for why the separate testing of functionalities is acceptable. Finally, this separate testing was also supplemented with a high-level integral check of each the diffusion and transport capabilities and applicable geometries. To accommodate cases which an analytic solution is not feasible, MCNP6.2 was relied upon to provide a higher-order reference solution. This therefore required that the capabilities within MCNP6.2 which were relied upon for this work are also verified in this work. No MCNP discrepancies were noted in this effort. Note that the MCNP6.2 verification included in this work does not stand as a full verification of MCNP6.2, but merely verifies the features used in verifying DIF3D. The verification effort identified no issues that are debilitating or otherwise impactful to design usage of DIF3D, and thus DIF3D version 11.0, release 3012 is considered verified. The types of issues that were identified were predominantly in the areas of: unclear documentation, software bugs which were inconsequential to final results, editing options which were ignored in favor of printing more information than requested, bugs in the outputs of intermediate results, or secondary output binary file information which was not present. While not a bug, this verification report also identified that the algorithm used to evaluate the peak fast flux in a nodal transport solution can be quite unreliable due to the polynomial order used and the location of the peak within the mesh. The authors of the report therefore recommend the usage of the EvaluateFlux software (distributed with ARC) as a more robust alternative.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Stochastic Verification by Analysis for Autonomous Systems Management Architecture (ASMA)
The Gateway Vehicle Systems Manager (VSM) is the top-level of a distributed, hierarchical software control system. VSM is data-driven and will make decisions related to mission, fault, resource management and vehicle control. These attributes combined with a high degree of autonomy make it susceptible to emergent behavior. In order to achieve the high level of confidence needed in this critical system, the VSM team has developed a multifaceted verification strategy employing traditional verification techniques, simulation, model checking, and runtime verification. Individual algorithms are verified using conventional testing and model checking using assume-guarantee contracts. A discrete event-based simulation approach is being developed to verify timelines. This presentation describes an enhancement to the verification approach using analysis to enhance system robustness by detecting and resolving the potential for emergent behavior. The verification by analysis employs a Software in the Loop (SITL) environment with real flight software executing on emulated processors, simulations of vehicle subsystems, flight dynamics, and human inputs. Since the possible input space and configuration data set are too large for exhaustive testing, a Monte Carlo approach is used to cover feasible scenarios, augmented with corner cases and known higher-risk scenarios. A key problem in using Monte Carlo-based system verification is evaluating test results to ensure that system behavior is correct. The presentation describes the approach the VSM team uses to monitor behavior for compliance with predetermined boundaries and to identify anomalous behavior for further analysis. This presentation describes the multi-level systems approach to verification, and the simulation-based layer that covers the feasible state space: 1. Overview of the Gateway VSM 2. Special challenges due to heterogeneous, hierarchical architecture 3. Modeling and simulation environment using flight software and system simulations 4. Developing input sets to ensure state-space coverage 5. Developing model and data configuration sets to ensure model coverage 6. Interpreting results without predetermined outcomes 7. Lessons learned and future work
Verification and Validation↗