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The entry guidance software functional requirements (requirements design phase), its architectural requirements (specifications design phase), and the entry guidance software verified code are discussed. It was found that the proper integration of designs at both the requirements and specifications levels are of high priority consideration.
Software Cost Reduction (SCR) methods are being utilized to analyze and verify selected parts of NASA's EOS-DIS Core System (ECS) requirements documentation. SCR is being used as a spot-inspection tool. Through this formal and systematic approach of the SCR requirements methods, insights as to whether the requirements are internally inconsistent or incomplete as the scenarios of intended usage evolve in the OC (Operations Concept) documentation. Thus, by modelling the scenarios and requirements as mode charts using the SCR methods, we have been able to identify problems within and between the documents.
The Isothermal Dendritic Growth Experiment (IDGE) is a microgravity materials science experiment scheduled to fly in the cargo bay of the shuttle on the United States Microgravity Payload (USMP) carrier. The experiment will be operated by real-time control software which will not only monitor and control onboard experiment hardware, but will also communicate, via downlink data and uplink commands, with the Payload Operations Control Center (POCC) at NASA George C. Marshall Space Flight Center (MSFC). The software development approach being used to implement this system began with software functional requirements specification. This was accomplished using the Yourdon/DeMarco methodology as supplemented by the Ward/Mellor real-time extensions. The requirements specification in combination with software prototyping was then used to generate a detailed design consisting of structure charts, module prologues, and Program Design Language (PDL) specifications. This detailed design will next be used to code the software, followed finally by testing against the functional requirements. The result will be a modular real-time control software system with traceability through every phase of the development process.
The Isothermal Dendritic Growth Experiment (IDGE) is a microgravity materials science experiment scheduled to fly in the cargo bay of the shuttle on the United States Microgravity Payload (USMP) carrier. The experiment will be operated by real-time control software which will not only monitor and control onboard experiment hardware, but will also communicate, via downlink data and unlink commands, with the Payload Operations Control Center (POCC) at NASA George C. Marshall Space Flight Center (MSFC). The software development approach being used to implement this system began with software functional requirements specification. This was accomplished using the Yourdon/DeMarco methodology as supplemented by the Ward/Mellor real-time extensions. The requirements specification in combination with software prototyping was then used to generate a detailed design consisting of structure charts, module prologues, and Program Design Language (PDL) specifications. This detailed design will next be used to code the software, followed finally by testing against the functional requirements. The result will be a modular real-time control software system with traceability through every phase of the development process.
The early problems and the solutions developed to provide the required quality software needed to support the space shuttle engine development program are described. The decision to use a programmable digital control system on the space shuttle engine was primarily based upon the need for a flexible control system capable of supporting the total engine mission on a large complex pump fed engine. The mission definition included all control phases from ground checkout through post shutdown propellant dumping. The flexibility of the controller through reprogrammable software allowed the system to respond to the technical challenges and innovation required to develop both the engine and controller hardware. This same flexibility, however, placed a severe strain on the capability of the software development and verification organization. The overall development program required that the software facility accommodate significant growth in both the software requirements and the number of software packages delivered. This challenge was met by reorganization and evolution in the process of developing and verifying software.
Over the past decade the marine energy industry has continued to grow and evolve, with new concepts and technologies constantly being pursued. Additionally, the field of computing is vastly different today than it was five or ten years ago. By utilizing advanced software and hardware architectures, like graphics processing units as well as parallelization and high-performance computing resources, software can produce higher quality outputs and a higher volume of outputs. These software and hardware resources can enable the marine energy community to exploit computational advancements from other research fields, which can include machine learning, differentiable programming, and controls co-design. Better integration of existing software and development of potential new software is necessary to take advantage of trends in modern computing and respond to the current and future needs of the marine energy community. In order to better understand the existing marine energy software landscape and industry needs, DOE's Water Power Technologies Office (WPTO) tasked Sandia National Laboratories and the National Renewable Energy Laboratory to update the needs assessment by identifying existing software gaps and software needs, and assisting WPTO in planning the next wave of marine energy software development. The proposed effort involved cataloguing and analyzing the available data on existing software related to marine energy. The marine energy software landscape has vastly changed in the last ten years. There are now nearly 230 different software packages utilized by the marine energy sector, compared to a decade ago when the Cardinal Engineering survey identified approximately 40 software packages. In 2012, the marine energy software landscape was captured in two tables, whereas the current marine energy software landscape required development of a software database to collect and categorize software.
Software engineering design and verification methods for developing expert systems are not yet well defined. Integration of expert system technology into software production environments will require effective software engineering methodologies to support the entire life cycle of expert systems. The software engineering methods used to design and verify an expert system, RENEX, is discussed. RENEX demonstrates autonomous rendezvous and proximity operations, including replanning trajectory events and subsystem fault detection, onboard a space vehicle during flight. The RENEX designers utilized a number of software engineering methodologies to deal with the complex problems inherent in this system. An overview is presented of the methods utilized. Details of the verification process receive special emphasis. The benefits and weaknesses of the methods for supporting the development life cycle of expert systems are evaluated, and recommendations are made based on the overall experiences with the methods.
To research, transfer, tailor, and support advanced techniques for the JPL environment.
Research into software development is required to reduce its production cost and to improve its quality. Modern software systems, such as the embedded software required for NASA's space station initiative, stretch current software engineering techniques. The requirements to build large, reliable, and maintainable software systems increases with time. Much theoretical and practical research is in progress to improve software engineering techniques. One such technique is to build a software system or environment which directly supports the software engineering process, i.e., the SAGA project, comprising the research necessary to design and build a software development which automates the software engineering process. Progress under SAGA is described.
The following topics are discussed in the context of software engineering: early use of the term; the 1968 NATO conference; Barry Boehm's definition; four requirements fo software engineering; and additional criteria for software engineering. Additionally, the four major requirements for software engineering--computer science, mathematics, engineering disciplines, and excellent communication skills--are discussed. The presentation is given in vugraph form.
We present a new software development paradigm that automates the derivation of implementations from requirements. In this paradigm, informally-stated requirements are expressed in a domain-specific requirements specification language. This language is machine-understable and requirements expressed in it are captured in a knowledge base. Once the requirements are captured, more detailed specifications and eventually implementations are derived by the system using transformational synthesis. A key characteristic of the process is that the required human intervention is in the form of providing problem- and domain-specific engineering knowledge, not in writing detailed implementations. We describe a prototype system that applies the paradigm in the realm of communication engineering: the prototype automatically generates implementations of buffers following analysis of the requirements on each buffer.
Required, recommended, and optional features are discussed that benefit preparation and evaluation of the load prediction equations in a strain-gage balance data analysis tool. First, milestones in the evolution of load prediction methods are reviewed so that suggested features can be put into a historical context. Afterwards, independent and dependent variable choices for the balance data analysis are discussed. Then, it is illustrated how three different metrics may be used for the identification of the root cause of an unwanted divergence of the load iterations that may be observed during an iterative load prediction. Finally, a list of features is provided to guide software development efforts.
As the requirements for producing software in the Ada language become a reality for projects such as the Space Station, a great amount of Ada-based program code will begin to emerge. Recognizing the potential for varying levels of quality to result in Ada programs, what is needed is a classification scheme that describes the quality of a software product whose source code exists in Ada form. A 5-level classification scheme is proposed that attempts to decompose this potentially broad spectrum of quality which Ada programs may possess. The number of classes and their corresponding names are not as important as the mere fact that there needs to be some set of criteria from which to evaluate programs existing in Ada. An exact criteria for each class is not presented, nor are any detailed suggestions of how to effectively implement this quality assessment. The idea of Ada-based software classification is introduced and a set of requirements from which to base further research and development is suggested.
The use of software fault trees for requirements identification and analysis in an Intrusion Detection System (IDS) is described.
Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.
The author describes the resource allocation and planning helper (RALPH) scheduling system developed at NASA's Jet Propulsion Laboratory. RALPH addresses the concerns of designing software systems to minimize the need to recode for changes and upgrades; this concern is acute when requirements are uncertain or changing. Determining requirements, understanding the problem, designing for change, and tradeoffs are also discussed.
A set of functional requirements for software configuration management (CM) and metrics reporting for Space Station Freedom ground systems software are described. This report is one of a series from a study of the interfaces among the Ground Systems Development Environment (GSDE), the development systems for the Space Station Training Facility (SSTF) and the Space Station Control Center (SSCC), and the target systems for SSCC and SSTF. The focus is on the CM of the software following delivery to NASA and on the software metrics that relate to the quality and maintainability of the delivered software. The CM and metrics requirements address specific problems that occur in large-scale software development. Mechanisms to assist in the continuing improvement of mission operations software development are described.
The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM aids the HERON software module in the evaluation of the optimal dispatch for the different IES components. Set-point trajectories are required to meet limits on both production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). To evaluate the feasibility of HERON generated set-points and to do so in an acceptable time, FARM employs reduced order models to represent the dynamic behavior of the systems to be dispatched. These surrogate models take the form of a linear dynamic system with sets of Linear Parameter Varying (LPV) matrices that are mapped to the system operating space. These matrices are derived from the trajectories of system state variables and system output variables during transients. The accuracy of LPV matrices depends on the selection of state variables. In previous reports, state variables were selected by adopting a complicated workflow requiring multiple software licenses and an advanced level of user expertise. In this report, a new workflow that automates the state variable selection process is presented. It significantly reduces the frequency of user interventions and does not require multiple software licenses. Each module in the new workflow is described in detail, and the input / output examples in each step of the workflow are provided. It was demonstrated that this workflow can greatly reduce the complexity of the state variable selection process, and that the updated FARM-Gamma and FARM-Delta validators can benefit from this workflow when solving the power dispatch problem of a representative IES test case. Finally, some code improvements that can further enhance the efficiency are suggested.