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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Lessons learned in deploying software estimation technology and tools

Developing a software product involves estimating various project parameters. This is typically done in the planning stages of the project when there is much uncertainty and very little information. Coming up with accurate estimates of effort, cost, schedule, and reliability is a critical problem faced by all software project managers. The use of estimation models and commercially available tools in conjunction with the best bottom-up estimates of software-development experts enhances the ability of a product development group to derive reasonable estimates of important project parameters. This paper describes the experience of the IBM Software Solutions (SWS) Toronto Laboratory in selecting software estimation models and tools and deploying their use to the laboratory's product development groups. It introduces the SLIM and COSTAR products, the software estimation tools selected for deployment to the product areas, and discusses the rationale for their selection. The paper also describes the mechanisms used for technology injection and tool deployment, and concludes with a discussion of important lessons learned in the technology and tool insertion process.

Panlilio-Yap, Nikki↗
NASA NTRS1980

As-built design specification for proportion estimate software subsystem

The Proportion Estimate Processor evaluates four estimation techniques in order to get an improved estimate of the proportion of a scene that is planted in a selected crop. The four techniques to be evaluated were provided by the techniques development section and are: (1) random sampling; (2) proportional allocation, relative count estimate; (3) proportional allocation, Bayesian estimate; and (4) sequential Bayesian allocation. The user is given two options for computation of the estimated mean square error. These are referred to as the cluster calculation option and the segment calculation option. The software for the Proportion Estimate Processor is operational on the IBM 3031 computer.

Obrien, S.↗
NASA NTRS2021

Online NASA Software Estimating Tools (ONSET): A Suite of Web-Based Cost Analysis Tools

This paper provides an overview of ONSET theOnline NASA Software Estimating Tools suite of web-basedcosts analysis tools. ONSET is comprised of the AnalogySoftware Cost Tool (ASCoT) [1], and the CubeSat Or MicrosatProbabilistic and Analogies Cost Tool (COMPACT) [2]. TheOnline NASA Space Estimation Tool (ONSET) has beendeveloped to provide a standardized platform for hosting webbasedNASA cost estimation tools on NASA ONCE (OneNASA Cost Engineering). It evolved from the BETA version ofASCoT (The NASA Analogy Software Cost Tool) which wasreleased in 2017 and was presented at the 2017 IEEEAerospace Conference. This first release of ONSET contains anew version of ASCoT as well as the first official release ofCOMPACT (CubeSat Or Microsat Probabilistic + AnalogiesCost Tool). In this paper we will provide an overview ofONSET, all its features and a high-level summary of bothASCoT and COMPACT. The heart of both tools arealgorithms for analogy-based estimation based on system levelinputs.

Johnson, James↗
NASA NTRS1999

Practical Methods for Estimating Software Systems Fault Content and Location

Over the past several years, we have developed techniques to discriminate between fault-prone software modules and those that are not, to estimate a software system's residual fault content, to identify those portions of a software system having the highest estimated number of faults, and to estimate the effects of requirements changes on software quality.

fault-prone discriminant function↗
NASA NTRS1994

Observation model and parameter partials for the JPL VLBI parameter estimation software MODEST, 19 94

This report is a revision of the document Observation Model and Parameter Partials for the JPL VLBI Parameter Estimation Software 'MODEST'---1991, dated August 1, 1991. It supersedes that document and its four previous versions (1983, 1985, 1986, and 1987). A number of aspects of the very long baseline interferometry (VLBI) model were improved from 1991 to 1994. Treatment of tidal effects is extended to model the effects of ocean tides on universal time and polar motion (UTPM), including a default model for nearly diurnal and semidiurnal ocean tidal UTPM variations, and partial derivatives for all (solid and ocean) tidal UTPM amplitudes. The time-honored 'K(sub 1) correction' for solid earth tides has been extended to include analogous frequency-dependent response of five tidal components. Partials of ocean loading amplitudes are now supplied. The Zhu-Mathews-Oceans-Anisotropy (ZMOA) 1990-2 and Kinoshita-Souchay models of nutation are now two of the modeling choices to replace the increasingly inadequate 1980 International Astronomical Union (IAU) nutation series. A rudimentary model of antenna thermal expansion is provided. Two more troposphere mapping functions have been added to the repertoire. Finally, corrections among VLBI observations via the model of Treuhaft and lanyi improve modeling of the dynamic troposphere. A number of minor misprints in Rev. 4 have been corrected.

Sovers, O. J.↗
NASA NTRS2018

Estimating Software Reliability for Space Launch Vehicles in Probabilistic Risk Assessment (PRA)

It is acutely recognized in the Probabilistic Risk assessment (PRA) field that software plays a defining role in overall system reliability for all modern systems across a wide variety of industries. Regardless if the software is embedded firmware for working components or elements, part of a Human-Machine-Interface, or automated command and control logic, the success of the software to fulfill its function under nominal and off-nominal environments will be a dominant contributor to system reliability. It is also recognized that software reliability prediction and estimation is one of the more challenging and questionable aspects of any PRA or system analyses due to the nature of software and its integration with physics based systems. Irrespective of this dichotomy, any incorporation of software reliability methods requires that the contributions are accountable, quantitative, and tractable. This paper provides a brief overview of software reliability methods, establishes some minimum requirements that the methods should incorporate for completeness, and provides a logic structure for applying software reliability. Model resolution will be discussed that supports current testing plans and trade studies. We will provide initial recommendations for use in the NASA PRA and present a future dynamic option for software and PRA. Space Launch Vehicle Software is recognized to be reliable in static conditions, yet relatively vulnerable to a set of failure modes in changing environments/flight phases. Two quantitative methods were chosen to incorporate software reliability into a Space Launch Vehicle PRA accounting for phase adjustments. One method predicts latent software failure using statistical methods, and the second provides estimates of coding errors and software operating system failures based on test and historical data, respectively. Software uncertainty will also be discussed. We determined that recommendations for PRA software reliability should be modeled at the software module level where multiple software components compose a module and combinations of the software architecture can lead to a functional failure.

Novack, Steven↗
NASA NTRS2018

Estimating Software Reliability for Space Launch Vehicles in Probabilistic Risk Assessment (PRA)

It is acutely recognized in the Probabilistic Risk Assessment (PRA) field that software plays a defining role in overall system reliability for all modern systems across a wide variety of industries. Regardless of whether the software is embedded firmware for working components or elements, part of a Human-Machine-Interface, or automated command and control logic, the success of the software to fulfill its function under nominal and off-nominal environments will be a dominant contributor to system reliability. It is also recognized that software reliability prediction and estimation is one of the more challenging and questionable aspects of any PRA or system analyses due to the nature of software and its integration with physics based systems. Irrespective of this dichotomy, any incorporation of software reliability methods requires that the contributions are accountable, quantitative, and tractable. This paper provides a brief overview of software reliability methods, establishes some minimum requirements that the methods should incorporate for completeness, and provides a logic structure for applying software reliability. Model resolution will be discussed that supports current testing plans and trade studies. We will provide initial recommendations for use in the National Aeronautics and Space Administration (NASA) PRA and present a future dynamic option for software and PRA. Space Launch Vehicle software is recognized to be reliable in static conditions, yet relatively vulnerable to a set of failure modes in changing environments/flight phases. Two quantitative methods were chosen to incorporate software reliability into a Space Launch Vehicle PRA accounting for phase adjustments. One method predicts latent software failure using statistical methods, and the second provides estimates of coding errors and software operating system failures based on test and historical data. Software uncertainty will also be discussed. It is determined that recommendations for PRA software reliability should be modeled at the software module level where multiple software components compose a module and combinations of the software architecture can lead to a functional failure.

Steven D. Novack↗
NASA NTRS2005

Fuzzy/Neural Software Estimates Costs of Rocket-Engine Tests

The Highly Accurate Cost Estimating Model (HACEM) is a software system for estimating the costs of testing rocket engines and components at Stennis Space Center. HACEM is built on a foundation of adaptive-network-based fuzzy inference systems (ANFIS) a hybrid software concept that combines the adaptive capabilities of neural networks with the ease of development and additional benefits of fuzzy-logic-based systems. In ANFIS, fuzzy inference systems are trained by use of neural networks. HACEM includes selectable subsystems that utilize various numbers and types of inputs, various numbers of fuzzy membership functions, and various input-preprocessing techniques. The inputs to HACEM are parameters of specific tests or series of tests. These parameters include test type (component or engine test), number and duration of tests, and thrust level(s) (in the case of engine tests). The ANFIS in HACEM are trained by use of sets of these parameters, along with costs of past tests. Thereafter, the user feeds HACEM a simple input text file that contains the parameters of a planned test or series of tests, the user selects the desired HACEM subsystem, and the subsystem processes the parameters into an estimate of cost(s).

Douglas, Freddie↗
NASA NTRS2006

NASA Software Estimating Tool (N-SET)

The goals of this project are to: Develop an early lifecycle software cost estimation tool leveraging existing data and capabilities Collect additional software data from: a) Jet Propulsion Laboratory; b) Goddard Space Flight Center; and c) Marshall Space Flight Center. Analyze, normalize, evaluate, stratify, and validate data. Create a calibrated, validated, and documented tool initially using available data and subsequently using newly collected data.

cost estimating tools↗
NASA NTRS1973

Misalignment estimation software system

A system of computer software, spacecraft, and ground system activity is described that enables spacecraft startrackers and inertial assemblies to be aligned and calibrated from the ground after the spacecraft has achieved orbit. The system generates in the uplink flow an exercise designed to render misalignments visible, and sends the exercise to the spacecraft where the spacecraft inserts the misalignment into the information in the form of attitude sensor error. The information is downlinked for processing into misalignment estimates to be used for correcting spacecraft model at data base.

Desjardins, R. L.↗
NASA NTRS1987

Development of confidence limits by pivotal functions for estimating software reliability

The utility of pivotal functions is established for assessing software reliability. Based on the Moranda geometric de-eutrophication model of reliability growth, confidence limits for attained reliability and prediction limits for the time to the next failure are derived using a pivotal function approach. Asymptotic approximations to the confidence and prediction limits are considered and are shown to be inadequate in cases where only a few bugs are found in the software. Departures from the assumed exponentially distributed interfailure times in the model are also investigated. The effect of these departures is discussed relative to restricting the use of the Moranda model.

Dotson, Kelly J.↗
NASA NTRS1987

Observation model and parameter partials for the JPL VLBI parameter estimation software MASTERFIT-1987

This report is a revision of the document of the same title (1986), dated August 1, which it supersedes. Model changes during 1986 and 1987 included corrections for antenna feed rotation, refraction in modelling antenna axis offsets, and an option to employ improved values of the semiannual and annual nutation amplitudes. Partial derivatives of the observables with respect to an additional parameter (surface temperature) are now available. New versions of two figures representing the geometric delay are incorporated. The expressions for the partial derivatives with respect to the nutation parameters have been corrected to include contributions from the dependence of UTI on nutation. The authors hope to publish revisions of this document in the future, as modeling improvements warrant.

Sovers, O. J.↗