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QUICK, an interactive software environment for engineering design, provides a programmable FORTRAN-like calculator interface to a wide range of data structures as well as both built-in and user created functions. QUICK also provides direct access to the operating systems of eight different machine architectures. The evolution of QUICK and a brief overview of the current version are presented.
A paradigm for scheduling computations on a network of multiprocessors using large-grain data flow scheduling at run time is described and analyzed. The computations to be scheduled must follow a static flow graph, while the schedule itself will be dynamic (i.e., determined at run time). Many applications characterized by static flow exist, and they include real-time control and digital signal processing. With the advent of computer-aided software engineering (CASE) tools for capturing software designs in dataflow-like structures, macro-dataflow scheduling becomes increasingly attractive, if not necessary. For parallel implementations, using the macro-dataflow method allows the scheduling to be insulated from the application designer and enables the maximum utilization of available resources. Further, by allowing multitasking, processor utilizations can approach 100 percent while they maintain maximum speedup. Extensive simulation studies are performed on 4-, 8-, and 16-processor architectures that reflect the effects of communication delays, scheduling delays, algorithm class, and multitasking on performance and speedup gains.
This Software Guide (SG) describes the software developed to test the Assessment Environment for Complex Systems (AECS) by the West Virginia High Technology Consortium (WVHTC) Foundation's Mission Systems Group (MSG) for the National Aeronautics and Space Administration (NASA) Aeronautics Research Mission Directorate (ARMD). This software is referred to as the AECS Test Project throughout the remainder of this document. AECS provides a framework for developing, simulating, testing, and analyzing modern avionics systems within an Integrated Modular Avionics (IMA) architecture. The purpose of the AECS Test Project is twofold. First, it provides a means to test the AECS hardware and system developed by MSG. Second, it provides an example project upon which future AECS research may be based. This Software Guide fully describes building, installing, and executing the AECS Test Project as well as its architecture and design. The design of the AECS hardware is described in the AECS Hardware Guide. Instructions on how to configure, build and use the AECS are described in the User's Guide. Sample AECS software, developed by the WVHTC Foundation, is presented in the AECS Software Guide. The AECS Hardware Guide, AECS User's Guide, and AECS Software Guide are authored by MSG. The requirements set forth for AECS are presented in the Statement of Work for the Assessment Environment for Complex Systems authored by NASA Dryden Flight Research Center (DFRC). The intended audience for this document includes software engineers, hardware engineers, project managers, and quality assurance personnel from WVHTC Foundation (the suppliers of the software), NASA (the customer), and future researchers (users of the software). Readers are assumed to have general knowledge in the field of real-time, embedded computer software development.
The Lunar Atmosphere Dust Environment Explorer (LADEE) mission orbited the moon in order to measure the density, composition, and time variability of the lunar dust environment. The successful mission launched September 7, 2013 and was de-orbited and impacted the moon's surface on April 17, 2014. The ground-side and onboard flight software for the mission was developed using a “Model-Based Software Engineering” (MBSE) methodology combined with strong reuse of Government and Commercial Off-The Shelf (G/COTS) components. Models of the spacecraft and flight software were developed in a graphical dynamics modeling package. Flight Software requirements were prototyped and refined using the simulated models. After the model was shown to work as desired in the simulation framework, C-code software was automatically generated from the models. The auto-generated software was then tested in real-time Processor-in-the-Loop and Hardware-in-the-Loop test beds. “Traveling Road Show” test beds were used for early integration tests with payloads and other subsystems. Traditional techniques for verifying computational sciences models were used to characterize the spacecraft simulation. A lightweight set of formal methods analysis, static analysis, formal inspection, and code coverage analyses were utilized to further reduce defects in the onboard flight software artifacts. These techniques were applied early and often in the development process, iteratively increasing the capabilities of software and fidelity of vehicle models and test beds.
This new release of MBDyn is a software engine that calculates the dynamics states of kinematic, rigid, or flexible multibody systems. An MBDyn multibody system may consist of multiple groups of articulated chains, trees, or closed-loop topologies. Transient topologies are handled through conservation of energy and momentum. The solution for rigid-body systems is exact, and several configurable levels of nonlinear term fidelity are available for flexible dynamics systems. The algorithms have been optimized for efficiency and can be used for both non-real-time (NRT) and real-time (RT) simulations. Interfaces are currently compatible with NASA's Trick Simulation Environment. This new release represents a significant advance in capability and ease of use. The two most significant new additions are an application programming interface (API) that clarifies and simplifies use of MBDyn, and a link-list infrastructure that allows a single MBDyn instance to propagate an arbitrary number of interacting groups of multibody top ologies. MBDyn calculates state and state derivative vectors for integration using an external integration routine. A Trickcompatible interface is provided for initialization, data logging, integration, and input/output.
The desire to predict the effort in developing or explaining the quality of software has led to the proposal of several metrics. As a step toward validating these metrics, the Software Engineering Laboratory (SEL) has analyzed the software science metrics, cyclomatic complexity, and various standard program measures for their relation to effort (including design through acceptance testing), development errors (both discrete and weighted according to the amount of time to locate and fix), and one another. The data investigated are collected from a project FORTRAN environment and examined across several projects at once, within individual projects and by reporting accuracy checks demonstrating the need to validate a database. When the data comes from individual programmers or certain validated projects, the metrics' correlations with actual effort seem to be strongest. For modules developed entirely by individual programmers, the validity ratios induce a statistically significant ordering of several of the metrics' correlations. When comparing the strongest correlations, neither software science's E metric cyclomatic complexity not source lines of code appears to relate convincingly better with effort than the others.
In this paper, the use of hybrid expert system shells and hybrid (i.e., algorithmic and heuristic) approaches for solving engineering problems is reported. Aspects of various engineering problem domains are reviewed for a number of examples with specific applications made to recently developed prototype expert systems. Based on this prototyping experience, critical evaluations of and comparisons between commercially available tools, and some research tools, in the United States and Australia, and their underlying problem-solving paradigms are made. Characteristics of the implementation tool and the engineering domain are compared and practical software engineering issues are discussed with respect to hybrid tools and approaches. Finally, guidelines are offered with the hope that expert system development will be less time consuming, more effective, and more cost-effective than it has been in the past.
Methods and aids for the management of software development projects are presented. The recommendations are based on analyses and experiences of the Software Engineering Laboratory (SEL) with flight dynamics software development. The management aspects of the following subjects are described: organizing the project, producing a development plan, estimating costs, scheduling, staffing, preparing deliverable documents, using management tools, monitoring the project, conducting reviews, auditing, testing, and certifying.
The design complexity of a software system may be characterized within a refinement level (e.g., data flow among modules), or between refinement levels (e.g., traceability between the specification and the design). We analyzed an existing set of data from NASA's Software Engineering Laboratory to test whether changing software modules with high design complexity requires more personnel effort than changing modules with low design complexity. By analyzing variables singly, we identified strong correlations between software design complexity and change effort for error corrections performed during the maintenance phase. By analyzing variables in combination, we found patterns which identify modules in which error corrections were costly to perform during the acceptance test phase.
The purpose of this paper is to provide an overview of the current trends and practices in small-satellite software verification and validation. This document is not intended to promote a specific software assurance method. Rather, it seeks to present an unbiased survey of software assurance methods used to verify and validate small satellite software and to make mention of the benefits and value of each approach. These methods include simulation and testing, verification and validation with model-based design, formal methods, and fault-tolerant software design with run-time monitoring. Although the literature reveals that simulation and testing has by far the longest legacy, model-based design methods are proving to be useful for software verification and validation. Some work in formal methods, though not widely used for any satellites, may offer new ways to improve small satellite software verification and validation. These methods need to be further advanced to deal with the state explosion problem and to make them more usable by small-satellite software engineers to be regularly applied to software verification. Last, it is explained how run-time monitoring, combined with fault-tolerant software design methods, provides an important means to detect and correct software errors that escape the verification process or those errors that are produced after launch through the effects of ionizing radiation.
This paper attempts to characterize and present a state of the art view of several quantitative models and metrics of the software life cycle. These models and metrics can be used to aid in managing and engineering software projects. They deal with various aspects of the software process and product, including resources allocation and estimation, changes and errors, size, complexity and reliability. Some indication is given of the extent to which the various models have been used and the success they have achieved.
At Goddard, engineers and scientists with a range of experience in science data systems are needed to employ new technologies and develop advances in capabilities for supporting new Earth and Space science research. Engineers with extensive experience in science data, software engineering and computer-information architectures are needed to lead and perform these activities. The increasing types and complexity of instrument data and emerging computer technologies coupled with the current shortage of computer engineers with backgrounds in science has led the need to develop a career path for science data systems engineers and architects.The current career path, in which undergraduate students studying various disciplines such as Computer Engineering or Physical Scientist, generally begins with serving on a development team in any of the disciplines where they can work in depth on existing Goddard data systems or serve with a specific NASA science team. There they begin to understand the data, infuse technologies, and begin to know the architectures of science data systems. From here the typical career involves peermentoring, on-the-job training or graduate level studies in analytics, computational science and applied science and mathematics. At the most senior level, engineers become subject matter experts and system architect experts, leading discipline-specific data centers and large software development projects. They are recognized as a subject matter expert in a science domain, they have project management expertise, lead standards efforts and lead international projects. A long career development remains necessary not only because of the breadth of knowledge required across physical sciences and engineering disciplines, but also because of the diversity of instrument data being developed today both by NASA and international partner agencies and because multidiscipline science and practitioner communities expect to have access to all types of observational data.This paper describes an approach to defining career-path guidance for college-bound high school and undergraduate engineering students, junior and senior engineers from various disciplines.
Guidelines and recommendations are presented for the collection of software development data. Motivation and planning for, and implementation and management of, a data collection effort are discussed. Topics covered include types, sources, and availability of data; methods and costs of data collection; types of analyses supported; and warnings and suggestions based on software engineering laboratory (SEL) experiences. This document is intended as a practical guide for software managers and engineers, abstracted and generalized from 5 years of SEL data collection.
The Simulation Software, KATE (Knowledgebase Autonomous Test Engineer), is used to demonstrate the automatic identification of faults in a system. The ACLO (Autonomous Cryogenics Loading Operation) project uses KATE to monitor and find faults in the loading of the cryogenics int o a vehicle fuel tank. The KATE software interfaces with the IHM (Integrated Health Management) systems bus to communicate with other systems that are part of ACLO. One system that KATE uses the IHM bus to communicate with is AIS (Advanced Inspection System). KATE will send messages to AIS when there is a detected anomaly. These messages include visual inspection of specific valves, pressure gauges and control messages to have AIS open or close manual valves. My goals include implementing the connection to the IHM bus within KATE and for the AIS project. I will also be working on implementing changes to KATE's Ul and implementing the physics objects in KATE that will model portions of the cryogenics loading operation.
State Analysis is a systems engineering methodology for the specification and design of control systems, developed at the Jet Propulsion Laboratory. The methodology emphasizes an analysis of the system under control in terms of States and their properties and behaviors and their effects on each other, a clear separation of the control system from the controlled system, cognizance in the control system of the controlled system's State, goal-based control built on constraining the controlled system's States, and disciplined techniques for State discovery and characterization. State Analysis (SA) introduces two key diagram types: State Effects and Goal Network diagrams. The team at JPL developed a tool for performing State Analysis. The tool includes a drawing capability, backed by a database that supports the diagram types and the organization of the elements of the SA models. But the tool does not support the usual activities of software engineering and design - a disadvantage, since systems to which State Analysis can be applied tend to be very software-intensive. This motivated the work described in this paper: the development of a preliminary Unified Modeling Language (UML) profile for State Analysis. Having this profile would enable systems engineers to specify a system using the methods and graphical language of State Analysis, which is easily linked with a larger system model in SysML (Systems Modeling Language), while also giving software engineers engaged in implementing the specified control system immediate access to and use of the SA model, in the same language, UML, used for other software design. That is, a State Analysis profile would serve as a shared modeling bridge between system and software models for the behavior aspects of the system. This paper begins with an overview of State Analysis and its underpinnings, followed by an overview of the mapping of SA constructs to the UML metamodel. It then delves into the details of these mappings and the constraints associated with them. Finally, we give an example of the use of the profile for expressing an example SA model.
Progress is reported on the development of SCOTTY, an expert knowledge-based system to automate the analysis procedure following test firings of the Space Shuttle Main Engine (SSME). The integration of a large-scale relational data base system, a computer graphics interface for experts and end-user engineers, potential extension of the system to flight engines, application of the system for training of newly-hired engineers, technology transfer to other engines, and the essential qualities of good software engineering practices for building expert knowledge-based systems are among the topics discussed.
This paper describes an approach of reusability of software engineering technology in the area of ground space system design. System engineers have lots of needs similar to software developers: sharing of a common data base, capitalization of knowledge, definition of a common design process, communication between different technical domains. Moreover system designers need to simulate dynamically their system as early as possible. Software development environments, methods and tools now become operational and widely used. Their architecture is based on a unique object base, a set of common management services and they host a family of tools for each life cycle activity. In late '92, CNES decided to develop a demonstrative software environment supporting some system activities. The design of ground space data processing systems was chosen as the application domain. ELISA (Integrated Software Environment for Architectures Specification) was specified as a 'demonstrator', i.e. a sufficient basis for demonstrations, evaluation and future operational enhancements. A process with three phases was implemented: system requirements definition, design of system architectures models, and selection of physical architectures. Each phase is composed of several activities that can be performed in parallel, with the provision of Commercial Off the Shelves Tools. ELISA has been delivered to CNES in January 94, currently used for demonstrations and evaluations on real projects (e.g. SPOT4 Satellite Control Center). It is on the way of new evolutions.
The first Conference of C Language Production Systems (CLIPS) hosted by the NASA-Lyndon B. Johnson Space Center in August 1990 is presented. Articles included engineering applications, intelligent tutors and training, intelligent software engineering, automated knowledge acquisition, network applications, verification and validation, enhancements to CLIPS, space shuttle quality control/diagnosis applications, space shuttle and real-time applications, and medical, biological, and agricultural applications.