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This paper presents a cooperative effort where the Software Engineering Institute and the Space Shuttle Onboard Software Project could experiment applying Statistical Process Control (SPC) analysis to inspection activities. The topics include: 1) SPC Collaboration Overview; 2) SPC Collaboration Approach and Results; and 3) Lessons Learned.
Design of large software systems requires rigorous application of software engineering methods covering all phases of the software process. Debugging during the early design phases is extremely important, because late bug-fixes are expensive. In this paper, we describe an approach which facilitates debugging of UML requirements and designs. The Unified Modeling Language (UML) is a set of notations for object-orient design of a software system. We have developed an algorithm which translates requirement specifications in the form of annotated sequence diagrams into structured statecharts. This algorithm detects conflicts between sequence diagrams and inconsistencies in the domain knowledge. After synthesizing statecharts from sequence diagrams, these statecharts usually are subject to manual modification and refinement. By using the "backward" direction of our synthesis algorithm. we are able to map modifications made to the statechart back into the requirements (sequence diagrams) and check for conflicts there. Fed back to the user conflicts detected by our algorithm are the basis for deductive-based debugging of requirements and domain theory in very early development stages. Our approach allows to generate explanations oil why there is a conflict and which parts of the specifications are affected.
Robust Software Engineering Group's case studies in aerospace software analysis a) Remote Agent; b) K9 Rover; c) Lessons learned; d) Research gaps; e) Verifying autonomy software.
This paper presents a discussion of the evolution of a sequencer from a simple EPICS (Experimental Physics and Industrial Control System) based sequencer into a complex implementation designed utilizing UML (Unified Modeling Language) methodologies and a CASE (Computer Aided Software Engineering) tool approach. The main purpose of the sequencer (called the IF Sequencer) is to provide overall control of the Keck Interferometer to enable science operations be carried out by a single operator (and/or observer). The interferometer links the two 10m telescopes of the W. M. Keck Observatory at Mauna Kea, Hawaii. The IF Sequencer is a high-level, multi-threaded, Hare1 finite state machine, software program designed to orchestrate several lower-level hardware and software hard real time subsystems that must perform their work in a specific and sequential order. The sequencing need not be done in hard real-time. Each state machine thread commands either a high-speed real-time multiple mode embedded controller via CORB A, or slower controllers via EPICS Channel Access interfaces. The overall operation of the system is simplified by the automation. The UML is discussed and our use of it to implement the sequencer is presented. The decision to use the Rhapsody product as our CASE tool is explained and reflected upon. Most importantly, a section on lessons learned is presented and the difficulty of integrating CASE tool automatically generated C++ code into a large control system consisting of multiple infrastructures is presented.
This is a tutorial on how to use the IKOS tool for static analysis of C/C++ code for flight critical system. The tutorial explains what static code analysis is, what kind of errors IKOS can find, and how to use the tool on single or multiple file projects. Simple examples are given to illustrate the use of the tool. This tutorial also describes the use of IKOS on real mission code, in this case, the flight software for the BioSentinel mission and the Troupe project in the Robust Software Engineering group.
G-DYN is a multi-body dynamic simulation software engine that automatically assembles and integrates equations of motion for arbitrarily connected multibody dynamic systems. The algorithm behind G-DYN is based on a primal-dual formulation of the dynamics that captures the position and velocity vectors (primal variables) of each body and the interaction forces (dual variables) between bodies, which are particularly useful for control and estimation analysis and synthesis. It also takes full advantage of the spare matrix structure resulting from the system dynamics to numerically integrate the equations of motion efficiently. Furthermore, the dynamic model for each body can easily be replaced without re-deriving the overall equations of motion, and the assembly of the equations of motion is done automatically. G-DYN proved an essential software tool in the simulation of spacecraft systems used for small celestial body surface sampling, specifically in simulating touch-and-go (TAG) maneuvers of a robotic sampling system from a comet and asteroid. It is used extensively in validating mission concepts for small body sample return, such as Comet Odyssey and Galahad New Frontiers proposals.
The engineering development of the National Aeronautics and Space Administration's (NASA) new Space Launch System (SLS) requires cross discipline teams with extensive knowledge of launch vehicle subsystems, information theory, and autonomous algorithms dealing with all operations from pre-launch through on orbit operations. The nominal and off-nominal characteristics of SLS's elements and subsystems must be understood and matched with the autonomous algorithm monitoring and mitigation capabilities for accurate control and response to abnormal conditions throughout all vehicle mission flight phases, including precipitating safing actions and crew aborts. This presents a large and complex systems engineering challenge, which is being addressed in part by focusing on the specific subsystems involved in the handling of off-nominal mission and fault tolerance with response management. Using traditional model-based system and software engineering design principles from the Unified Modeling Language (UML) and Systems Modeling Language (SysML), the Mission and Fault Management (M&FM) algorithms for the vehicle are crafted and vetted in Integrated Development Teams (IDTs) composed of multiple development disciplines such as Systems Engineering (SE), Flight Software (FSW), Safety and Mission Assurance (S&MA) and the major subsystems and vehicle elements such as Main Propulsion Systems (MPS), boosters, avionics, Guidance, Navigation, and Control (GNC), Thrust Vector Control (TVC), and liquid engines. These model-based algorithms and their development lifecycle from inception through FSW certification are an important focus of SLS's development effort to further ensure reliable detection and response to off-nominal vehicle states during all phases of vehicle operation from pre-launch through end of flight. To test and validate these M&FM algorithms a dedicated test-bed was developed for full Vehicle Management End-to-End Testing (VMET). For addressing fault management (FM) early in the development lifecycle for the SLS program, NASA formed the M&FM team as part of the Integrated Systems Health Management and Automation Branch under the Spacecraft Vehicle Systems Department at the Marshall Space Flight Center (MSFC). To support the development of the FM algorithms, the VMET developed by the M&FM team provides the ability to integrate the algorithms, perform test cases, and integrate vendor-supplied physics-based launch vehicle (LV) subsystem models. Additionally, the team has developed processes for implementing and validating the M&FM algorithms for concept validation and risk reduction. The flexibility of the VMET capabilities enables thorough testing of the M&FM algorithms by providing configurable suites of both nominal and off-nominal test cases to validate the developed algorithms utilizing actual subsystem models such as MPS, GNC, and others. One of the principal functions of VMET is to validate the M&FM algorithms and substantiate them with performance baselines for each of the target vehicle subsystems in an independent platform exterior to the flight software test and validation processes. In any software development process there is inherent risk in the interpretation and implementation of concepts from requirements and test cases into flight software compounded with potential human errors throughout the development and regression testing lifecycle. Risk reduction is addressed by the M&FM group but in particular by the Analysis Team working with other organizations such as S&MA, Structures and Environments, GNC, Orion, Crew Office, Flight Operations, and Ground Operations by assessing performance of the M&FM algorithms in terms of their ability to reduce Loss of Mission (LOM) and Loss of Crew (LOC) probabilities. In addition, through state machine and diagnostic modeling, analysis efforts investigate a broader suite of failure effects and associated detection and responses to be tested in VMET to ensure reliable failure detection, and confirm responses do not create additional risks or cause undesired states through interactive dynamic effects with other algorithms and systems. VMET further contributes to risk reduction by prototyping and exercising the M&FM algorithms early in their implementation and without any inherent hindrances such as meeting FSW processor scheduling constraints due to their target platform - the ARINC 6535-partitioned Operating System, resource limitations, and other factors related to integration with other subsystems not directly involved with M&FM such as telemetry packing and processing. The baseline plan for use of VMET encompasses testing the original M&FM algorithms coded in the same C++ language and state machine architectural concepts as that used by FSW. This enables the development of performance standards and test cases to characterize the M&FM algorithms and sets a benchmark from which to measure their effectiveness and performance in the exterior FSW development and test processes. This paper is outlined in a systematic fashion analogous to a lifecycle process flow for engineering development of algorithms into software and testing. Section I describes the NASA SLS M&FM context, presenting the current infrastructure, leading principles, methods, and participants. Section II defines the testing philosophy of the M&FM algorithms as related to VMET followed by section III, which presents the modeling methods of the algorithms to be tested and validated in VMET. Its details are then further presented in section IV followed by Section V presenting integration, test status, and state analysis. Finally, section VI addresses the summary and forward directions followed by the appendices presenting relevant information on terminology and documentation.
BEST3D is advanced engineering software system for three-dimensional thermal and stress analyses, particularly of components of hot sections of gas-turbine engines. Utilizes boundary element method, offering, in many situations, more accuracy, efficiency, and ease of use than finite element method. Performs engineering analyses of following types: elastic, heat transfer, plastic, forced vibration, free vibration, and transient elastodynamic. Written in FORTRAN 77.
Continuous Risk Management (CM) is a software engineering practice with processes, methods, and tools for managing risk in a project. It provides a controlled environment for practical decision making, in order to assess continually what could go wrong, determine which risk are important to deal with, implement strategies to deal with those risk and assure the measure effectiveness of the implemented strategies. Continuous Risk Management provides many training workshops and courses to teach the staff how to implement risk management to their various experiments and projects. The steps of the CRM process are identification, analysis, planning, tracking, and control. These steps and the various methods and tools that go along with them, identification, and dealing with risk is clear-cut. The office that I worked in was the Risk Management Office (RMO). The RMO at NASA works hard to uphold NASA s mission of exploration and advancement of scientific knowledge and technology by defining and reducing program risk. The RMO is one of the divisions that fall under the Safety and Assurance Directorate (SAAD). I worked under Cynthia Calhoun, Flight Software Systems Engineer. My task was to develop a help screen for the Continuous Risk Management Implementation Tool (RMIT). The Risk Management Implementation Tool will be used by many NASA managers to identify, analyze, track, control, and communicate risks in their programs and projects. The RMIT will provide a means for NASA to continuously assess risks. The goals and purposes for this tool is to provide a simple means to manage risks, be used by program and project managers throughout NASA for managing risk, and to take an aggressive approach to advertise and advocate the use of RMIT at each NASA center.
The engineering development of the new Space Launch System (SLS) launch vehicle requires cross discipline teams with extensive knowledge of launch vehicle subsystems, information theory, and autonomous algorithms dealing with all operations from pre-launch through on orbit operations. The characteristics of these spacecraft systems must be matched with the autonomous algorithm monitoring and mitigation capabilities for accurate control and response to abnormal conditions throughout all vehicle mission flight phases, including precipitating safing actions and crew aborts. This presents a large and complex system engineering challenge, which is being addressed in part by focusing on the specific subsystems involved in the handling of off-nominal mission and fault tolerance with response management. Using traditional model based system and software engineering design principles from the Unified Modeling Language (UML) and Systems Modeling Language (SysML), the Mission and Fault Management (M&FM) algorithms for the vehicle are crafted and vetted in specialized Integrated Development Teams (IDTs) composed of multiple development disciplines such as Systems Engineering (SE), Flight Software (FSW), Safety and Mission Assurance (S&MA) and the major subsystems and vehicle elements such as Main Propulsion Systems (MPS), boosters, avionics, Guidance, Navigation, and Control (GNC), Thrust Vector Control (TVC), and liquid engines. These model based algorithms and their development lifecycle from inception through Flight Software certification are an important focus of this development effort to further insure reliable detection and response to off-nominal vehicle states during all phases of vehicle operation from pre-launch through end of flight. NASA formed a dedicated M&FM team for addressing fault management early in the development lifecycle for the SLS initiative. As part of the development of the M&FM capabilities, this team has developed a dedicated testbed that integrates specific M&FM algorithms, specialized nominal and off-nominal test cases, and vendor-supplied physics-based launch vehicle subsystem models. Additionally, the team has developed processes for implementing and validating these algorithms for concept validation and risk reduction for the SLS program. The flexibility of the Vehicle Management End-to-end Testbed (VMET) enables thorough testing of the M&FM algorithms by providing configurable suites of both nominal and off-nominal test cases to validate the developed algorithms utilizing actual subsystem models such as MPS. The intent of VMET is to validate the M&FM algorithms and substantiate them with performance baselines for each of the target vehicle subsystems in an independent platform exterior to the flight software development infrastructure and its related testing entities. In any software development process there is inherent risk in the interpretation and implementation of concepts into software through requirements and test cases into flight software compounded with potential human errors throughout the development lifecycle. Risk reduction is addressed by the M&FM analysis group working with other organizations such as S&MA, Structures and Environments, GNC, Orion, the Crew Office, Flight Operations, and Ground Operations by assessing performance of the M&FM algorithms in terms of their ability to reduce Loss of Mission and Loss of Crew probabilities. In addition, through state machine and diagnostic modeling, analysis efforts investigate a broader suite of failure effects and associated detection and responses that can be tested in VMET to ensure that failures can be detected, and confirm that responses do not create additional risks or cause undesired states through interactive dynamic effects with other algorithms and systems. VMET further contributes to risk reduction by prototyping and exercising the M&FM algorithms early in their implementation and without any inherent hindrances such as meeting FSW processor scheduling constraints due to their target platform - ARINC 653 partitioned OS, resource limitations, and other factors related to integration with other subsystems not directly involved with M&FM such as telemetry packing and processing. The baseline plan for use of VMET encompasses testing the original M&FM algorithms coded in the same C++ language and state machine architectural concepts as that used by Flight Software. This enables the development of performance standards and test cases to characterize the M&FM algorithms and sets a benchmark from which to measure the effectiveness of M&FM algorithms performance in the FSW development and test processes.
Software itself is not hazardous, but since software and hardware share common interfaces there is an opportunity for software to create hazards. Further, these software systems are complex, and proven methods for the design, analysis, and measurement of software safety are not yet available. Some past software failures, future NASA software trends, software engineering methods, and tools and techniques for various software safety analyses are reviewed. Recommendations to NASA are made based on this review.
The interdependence of the U.S.-Japanese economies makes it imperative that we in the United States understand how business and technology developments take place in Japan. We can gain insight into these developments in software engineering by studying the context in which Japanese software is developed, the practices that are used, the problems encountered, the setting surrounding these problems, and the resolution of these problems. Context includes the technological and sociological characteristics of the software development environment, the software processes applied, personnel involved in the development process, and the corporate and social culture surrounding the development. Presented in this paper is a summary of results of a study that addresses these issues. Data for this study was collected during a three month visit to Japan where the author interviewed 20 software managers representing nine companies involved in developing software in Japan. These data are compared to similar data from the United States in which 12 managers from five companies were interviewed.
The Software Engineering Laboratory uses a paradigm for improving the software process and product, called the quality improvement paradigm. This paradigm has evolved over the past 18 years, along with our software development processes and product. Since 1976, when we first began the SEL, we have learned a great deal about improving the software process and product, making a great many mistakes along the way. Quality improvement paradigm, as it is currently defined, can be broken up into six steps: characterize the current project and its environment with respect to the appropriate models and metrics; set the quantifiable goals for successful project performance and improvement; choose the appropriate process model and supporting methods and tools for this project; execute the processes, construct the products, and collect, validate, and analyze the data to provide real-time feedback for corrective action; analyze the data to evaluate the current practices, determine problems, record findings, and make recommendations for future project improvements; and package the experience gained in the form of updated and refined models and other forms of structured knowledge gained from this and prior projects and save it in an experience base to be reused on future projects.
This paper describes the design, development, and testing of the EDL program from the perspective of the software engineer. We briefly cover the overall MSL flight software organization, and then the organization of EDL itself. We discuss the timeline, the structure of the GNC code (but not the algorithms as they are covered elsewhere in this conference) and the command and telemetry interfaces. Finally, we cover testing and the influence that testability had on the EDL flight software design.
A substantial amount of software engineering research effort was focused on the development of software estimation models. A measure of lines of code was derived based on the origin of the delivered code, L sub dev = N + E + 0.2 (S + O) (where L sub dev = developed lines of code, N = newly implemented lines of code, E = extensively modified lines of code, S = slightly modified lines of code, and O = old unchanged lines of code) that is substituted in the following equation H sub s = aL sup b (where H sub s = staff-hours of effort, L = lines of code, a = a constant, and b = a constant). The limitations of this model are discussed and some alternative estimation models that can be used earlier in the development process are proposed.
Abstract data types and object-oriented design are active research areas in computer science and software engineering. Much of the interest is aimed at new software development. Abstract data type packages developed for a discontinued software project were used to improve a real-time data-acquisition system under maintenance. The result saved effort and contributed to a significant improvement in the performance, maintainability, and reliability of the Goldstone Solar System Radar Data Acquisition System.
Due to the large number of product, project, and people parameters which impact large custom software development efforts, measurement of software product quality is a complex undertaking. Furthermore, the absolute perspective from which quality is measured (customer satisfaction) is intangible. While we probably can't say what the absolute quality of a software product is, we can determine the relative quality, the adequacy of this quality with respect to pragmatic considerations, and identify good and bad trends during development. While no two software engineers will ever agree on an optimum definition of software quality, they will agree that the most important perspective of software quality is its ease of change. We can call this flexibility, adaptability, or some other vague term, but the critical characteristic of software is that it is soft. The easier the product is to modify, the easier it is to achieve any other software quality perspective. This paper presents objective quality metrics derived from consistent lifecycle perspectives of rework which, when used in concert with an evolutionary development approach, can provide useful insight to produce better quality per unit cost/schedule or to achieve adequate quality more efficiently. The usefulness of these metrics is evaluated by applying them to a large, real world, Ada project.
Integrated Environments for Large, Complex Systems is the theme for the RICIS symposium of 1988. Distinguished professionals from industry, government, and academia have been invited to participate and present their views and experiences regarding research, education, and future directions related to this topic. Within RICIS, more than half of the research being conducted is in the area of Computer Systems and Software Engineering. The focus of this research is on the software development life-cycle for large, complex, distributed systems. Within the education and training component of RICIS, the primary emphasis has been to provide education and training for software professionals.