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All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Report on Automated Semantic Analysis of Scientific and Engineering Codes

The loss of the Mars Climate Orbiter due to a software error reveals what insiders know: software development is difficult and risky because, in part, current practices do not readily handle the complex details of software. Yet, for scientific software development the MCO mishap represents the tip of the iceberg; few errors are so public, and many errors are avoided with a combination of expertise, care, and testing during development and modification. Further, this effort consumes valuable time and resources even when hardware costs and execution time continually decrease. Software development could use better tools! This lack of tools has motivated the semantic analysis work explained in this report. However, this work has a distinguishing emphasis; the tool focuses on automated recognition of the fundamental mathematical and physical meaning of scientific code. Further, its comprehension is measured by quantitatively evaluating overall recognition with practical codes. This emphasis is necessary if software errors-like the MCO error-are to be quickly and inexpensively avoided in the future. This report evaluates the progress made with this problem. It presents recommendations, describes the approach, the tool's status, the challenges, related research, and a development strategy.

Stewart. Maark E. M.↗
NASA NTRS2014

Test Driven Development of Scientific Models

Test-Driven Development (TDD), a software development process that promises many advantages for developer productivity and software reliability, has become widely accepted among professional software engineers. As the name suggests, TDD practitioners alternate between writing short automated tests and producing code that passes those tests. Although this overly simplified description will undoubtedly sound prohibitively burdensome to many uninitiated developers, the advent of powerful unit-testing frameworks greatly reduces the effort required to produce and routinely execute suites of tests. By testimony, many developers find TDD to be addicting after only a few days of exposure, and find it unthinkable to return to previous practices.After a brief overview of the TDD process and my experience in applying the methodology for development activities at Goddard, I will delve more deeply into some of the challenges that are posed by numerical and scientific software as well as tools and implementation approaches that should address those challenges.

Overview of TDD Process↗
NASA NTRS1981

The SIFT computer and its development

Software Implemented Fault Tolerance (SIFT) is an aircraft control computer designed to allow failure probability of less than 10 to the -10th/hour. The system is based on advanced fault-tolerance computing and validation methodology. Since confirmation of reliability by observation is essentially impossible, system reliability is estimated by a Markov model. A mathematical proof is used to justify the validity of the Markov model. System design is represented by a hierarchy of abstract models, and the design proof comprises mathematical proofs that each model is, in fact, an elaboration of the next more abstract model.

Goldberg, J.↗
NASA NTRS2006

The NASA Software Research Infusion Initiative: Successful Technology Transfer for Software Assurance

New processes, methods and tools are constantly appearing in the field of software engineering. Many of these augur great potential in improving software development processes, resulting in higher quality software with greater levels of assurance. However, there are a number of obstacles that impede their infusion into software development practices. These are the recurring obstacles common to many forms of research. Practitioners cannot readily identify the emerging techniques that may most benefit them, and cannot afford to risk time and effort in evaluating and experimenting with them while there is still uncertainty about whether they will have payoff in this particular context. Similarly, researchers cannot readily identify those practitioners whose problems would be amenable to their techniques and lack the feedback from practical applications necessary to help them to evolve their techniques to make them more likely to be successful. This paper describes an ongoing effort conducted by a software engineering research infusion team, and the NASA Research Infusion Initiative, established by NASA s Software Engineering Initiative, to overcome these obstacles.

Hinchey, Michael G.↗
NASA NTRS1984

SAGA: A project to automate the management of software production systems

The project to automate the management of software production systems is described. The SAGA system is a software environment that is designed to support most of the software development activities that occur in a software lifecycle. The system can be configured to support specific software development applications using given programming languages, tools, and methodologies. Meta-tools are provided to ease configuration. Several major components of the SAGA system are completed to prototype form. The construction methods are described.

Campbell, R. H.↗
NASA NTRS1984

A methodology for collecting valid software engineering data

An effective data collection method for evaluating software development methodologies and for studying the software development process is described. The method uses goal-directed data collection to evaluate methodologies with respect to the claims made for them. Such claims are used as a basis for defining the goals of the data collection, establishing a list of questions of interest to be answered by data analysis, defining a set of data categorization schemes, and designing a data collection form. Feasibility of the data collection methodology was demonstrated by applying it to five different projects in two different environments (other NRL Reports). The application showed that the methodology was both feasible and useful.

Basili, V. R.↗
NASA NTRS1991

Lessons learned in the development of the Hubble Space Telescope software

An effort is made to document what was successful in the HST software development, and to present a retrospective analysis indicating what could have been done more efficiently. Consideration is given to systems engineering and design definition, standards and tools, system architecture and design, operational considerations, and reuse and adaptation of existing resources.

Harrington, Michael↗
NASA NTRS2011

Microsoft Kinect Sensor Evaluation

My summer project evaluates the Kinect game sensor input/output and its suitability to perform as part of a human interface for a spacecraft application. The primary objective is to evaluate, understand, and communicate the Kinect system's ability to sense and track fine (human) position and motion. The project will analyze the performance characteristics and capabilities of this game system hardware and its applicability for gross and fine motion tracking. The software development kit for the Kinect was also investigated and some experimentation has begun to understand its development environment. To better understand the software development of the Kinect game sensor, research in hacking communities has brought a better understanding of the potential for a wide range of personal computer (PC) application development. The project also entails the disassembly of the Kinect game sensor. This analysis would involve disassembling a sensor, photographing it, and identifying components and describing its operation.

Billie, Glennoah↗
NASA NTRS2006

Software Engineering Research/Developer Collaborations in 2005

In CY 2005, three collaborations between software engineering technology providers and NASA software development personnel deployed three software engineering technologies on NASA development projects (a different technology on each project). The main purposes were to benefit the projects, infuse the technologies if beneficial into NASA, and give feedback to the technology providers to improve the technologies. Each collaboration project produced a final report. Section 2 of this report summarizes each project, drawing from the final reports and communications with the software developers and technology providers. Section 3 indicates paths to further infusion of the technologies into NASA practice. Section 4 summarizes some technology transfer lessons learned. Also included is an acronym list.

Pressburger, Tom↗
NASA NTRS1981

Software engineering standards and practices

Guidelines are presented for the preparation of a software development plan. The various phases of a software development project are discussed throughout its life cycle including a general description of the software engineering standards and practices to be followed during each phase.

Durachka, R. W.↗
NASA NTRS1990

Software Quality Assurance Audits Guidebooks

The growth in cost and importance of software to NASA has caused NASA to address the improvement of software development across the agency. One of the products of this program is a series of guidebooks that define a NASA concept of the assurance processes that are used in software development. The Software Assurance Guidebook, NASA-GB-A201, issued in September, 1989, provides an overall picture of the NASA concepts and practices in software assurance. Second level guidebooks focus on specific activities that fall within the software assurance discipline, and provide more detailed information for the manager and/or practitioner. This is the second level Software Quality Assurance Audits Guidebook that describes software quality assurance audits in a way that is compatible with practices at NASA Centers.

Source record↗
NASA NTRS2004

Survey of Software Assurance Techniques for Highly Reliable Systems

This document provides a survey of software assurance techniques for highly reliable systems including a discussion of relevant safety standards for various industries in the United States and Europe, as well as examples of methods used during software development projects. It contains one section for each industry surveyed: Aerospace, Defense, Nuclear Power, Medical Devices and Transportation. Each section provides an overview of applicable standards and examples of a mission or software development project, software assurance techniques used and reliability achieved.

Nelson, Stacy↗
NASA NTRS1986

Application and systems software in Ada: Development experiences

In its most basic sense software development involves describing the tasks to be solved, including the given objects and the operations to be performed on those objects. Unfortunately, the way people describe objects and operations usually bears little resemblance to source code in most contemporary computer languages. There are two ways around this problem. One is to allow users to describe what they want the computer to do in everyday, typically imprecise English. The PRODOC methodology and software development environment is based on a second more flexible and possibly even easier to use approach. Rather than hiding program structure, PRODOC represents such structure graphically using visual programming techniques. In addition, the program terminology used in PRODOC may be customized so as to match the way human experts in any given application area naturally describe the relevant data and operations. The PRODOC methodology is described in detail.

Kuschill, Jim↗
NASA NTRS2018

NOS3: NASA Operational Simulator for Small Satellites

The NASA Operational Simulator for Small Satellites (NOS3) is a suite of open-source software tools to aid in areas such as software development, integration & test (I&T), mission operations/training, verification and validation (V&V), and software systems check-out. NOS3 provides a software development environment, a multi-target build system, operational interface/ground software, dynamics and environment simulations, and software-based hardware models. NOS3 has just recently been open-sourced by NASA and is available for immediate use. It enables the development of flight software (FSW) early in the project life cycle when hardware availability is limited. Small satellite development suffers from extensive lead times on many of the commercial-off-the-shelf (COTS) components as well as limited funding for engineering test units (ETUs). To alleviate the need to provide a hardware test-bed for each developer/tester, NOS3 hardware models are based upon characteristic data or manufacturer's data sheets for each individual component. The NOS3 hardware models' fidelity is such that FSW executes unaware that physical hardware is not present. This allows FSW binaries to be compiled for both the simulation environment and the flight computer without changing the FSW source code. For hardware models that provide data which is dependent upon the environment and spacecraft dynamics, such as a GPS receiver or magnetometer, an open-source tool from NASA GSFC (42 Spacecraft Simulator) is used to provide the necessary data. The underlying infrastructure used to transfer messages between FSW and the hardware models can also be used to monitor, intercept, and inject messages, which has proven to be beneficial for V&V of larger missions such as James Webb Space Telescope (JWST). As hardware is selected and becomes available, drivers can be added to the NOS3 environment to enable hardware-in-the-loop (HWIL) testing. When strict time synchronization is not vital, any number of combinations of hardware components and software-based models can be tested. NOS3 was actively used for FSW development and component testing of the Simulation-to-Flight 1 (STF-1) CubeSat and the Lunar IceCube CubeSat. As NOS3 matures, hardware models have been added for common small satellite components such as GPS receivers, electrical power systems and batteries, and antenna systems.

Suder, Mark↗
NASA NTRS2016

NASA Operational Simulator for Small Satellites: Tools for Software Based Validation and Verification of Small Satellites

The NASA Operational Simulator for Small Satellites (NOS3) is a suite of tools to aid in areas such as software development, integration test (IT), mission operations training, verification and validation (VV), and software systems check-out. NOS3 provides a software development environment, a multi-target build system, an operator interface-ground station, dynamics and environment simulations, and software-based hardware models. NOS3 enables the development of flight software (FSW) early in the project life cycle, when access to hardware is typically not available. For small satellites there are extensive lead times on many of the commercial-off-the-shelf (COTS) components as well as limited funding for engineering test units (ETU). Considering the difficulty of providing a hardware test-bed to each developer tester, hardware models are modeled based upon characteristic data or manufacturers data sheets for each individual component. The fidelity of each hardware models is such that FSW executes unaware that physical hardware is not present. This allows binaries to be compiled for both the simulation environment, and the flight computer, without changing the FSW source code. For hardware models that provide data dependent on the environment, such as a GPS receiver or magnetometer, an open-source tool from NASA GSFC (42 Spacecraft Simulation) is used to provide the necessary data. The underlying infrastructure used to transfer messages between FSW and the hardware models can also be used to monitor, intercept, and inject messages, which has proven to be beneficial for VV of larger missions such as James Webb Space Telescope (JWST). As hardware is procured, drivers can be added to the environment to enable hardware-in-the-loop (HWIL) testing. When strict time synchronization is not vital, any number of combinations of hardware components and software-based models can be tested. The open-source operator interface used in NOS3 is COSMOS from Ball Aerospace. For testing, plug-ins are implemented in COSMOS to control the NOS3 simulations, while the command and telemetry tools available in COSMOS are used to communicate with FSW. NOS3 is actively being used for FSW development and component testing of the Simulation-to-Flight 1 (STF-1) CubeSat. As NOS3 matures, hardware models have been added for common CubeSat components such as Novatel GPS receivers, ClydeSpace electrical power systems and batteries, ISISpace antenna systems, etc. In the future, NASA IVV plans to distribute NOS3 to other CubeSat developers and release the suite to the open-source community.

Verification↗
NASA NTRS2022

GRC MILab Software: Quick Start Guide

This document provides detailed installation and operating instructions for the GRC MILab Excel Add-In software developed at the NASA Glenn Research Center. The software described has been implemented to facilitate the process of importing into Microsoft Excel and analyzing materials test data from a wide range of materials tests. All resulting data is then ready for automated upload to the relevant table of the GRC Materials Intelligence (MI) database. This new software represents an update to the original MILab software developed by Granta Design Ltd.—a company specializing in materials software, data, and databases—for members of the Materials Data Management Consortium (MDMC), a collaboration between Granta, ASM International, NASA Glenn, and several other materials-oriented corporations and government agencies in the aerospace and defense industries. The updated software consists of the addition of two test type modules, the Generic and Generic Cyclic modules, with both representing a generalization of the original software. The Generic module supports the import and analysis of multiaxial data from any sequence of tensile, compression, relaxation, and/or creep test stages; and the Generic Cyclic module expands the functionality to include repeated sequences during cyclic testing. During processing, all imported data and analysis results are formatted by the software so as to be ready for immediate automated upload to the MI database, ensuring minimal overhead on the part of the user and access to persistent and reliable data for all relevant personnel.

Quick Start Guide↗
NASA NTRS2009

Modeling Guidelines for Code Generation in the Railway Signaling Context

Modeling guidelines constitute one of the fundamental cornerstones for Model Based Development. Their relevance is essential when dealing with code generation in the safety-critical domain. This article presents the experience of a railway signaling systems manufacturer on this issue. Introduction of Model-Based Development (MBD) and code generation in the industrial safety-critical sector created a crucial paradigm shift in the development process of dependable systems. While traditional software development focuses on the code, with MBD practices the focus shifts to model abstractions. The change has fundamental implications for safety-critical systems, which still need to guarantee a high degree of confidence also at code level. Usage of the Simulink/Stateflow platform for modeling, which is a de facto standard in control software development, does not ensure by itself production of high-quality dependable code. This issue has been addressed by companies through the definition of modeling rules imposing restrictions on the usage of design tools components, in order to enable production of qualified code. The MAAB Control Algorithm Modeling Guidelines (MathWorks Automotive Advisory Board)[3] is a well established set of publicly available rules for modeling with Simulink/Stateflow. This set of recommendations has been developed by a group of OEMs and suppliers of the automotive sector with the objective of enforcing and easing the usage of the MathWorks tools within the automotive industry. The guidelines have been published in 2001 and afterwords revisited in 2007 in order to integrate some additional rules developed by the Japanese division of MAAB [5]. The scope of the current edition of the guidelines ranges from model maintainability and readability to code generation issues. The rules are conceived as a reference baseline and therefore they need to be tailored to comply with the characteristics of each industrial context. Customization of these recommendations has been performed for the automotive control systems domain in order to enforce code generation [7]. The MAAB guidelines have been found profitable also in the aerospace/avionics sector [1] and they have been adopted by the MathWorks Aerospace Leadership Council (MALC). General Electric Transportation Systems (GETS) is a well known railway signaling systems manufacturer leading in Automatic Train Protection (ATP) systems technology. Inside an effort of adopting formal methods within its own development process, GETS decided to introduce system modeling by means of the MathWorks tools [2], and in 2008 chose to move to code generation. This article reports the experience performed by GETS in developing its own modeling standard through customizing the MAAB rules for the railway signaling domain and shows the result of this experience with a successful product development story.

Ferrari, Alessio↗
NASA NTRS1996

Development of software for the MSFC solar vector magnetograph

The Marshall Space Flight Center Solar Vector Magnetograph is a special purpose telescope used to measure the vector magnetic field in active areas on the surface of the sun. This instrument measures the linear and circular polarization intensities (the Stokes vectors Q, U and V) produced by the Zeeman effect on a specific spectral line due to the solar magnetic field from which the longitudinal and transverse components of the magnetic field may be determined. Beginning in 1990 as a Summer Faculty Fellow in project JOVE and continuing under NASA Grant NAG8-1042, the author has been developing computer software to perform these computations, first using a DEC MicroVAX system equipped with a high speed array processor, and more recently using a DEC AXP/OSF system. This summer's work is a continuation of this development.

Kineke, Jack↗