Engineering topics
Publications and source records attributed to Feather, Martin S..
We report on a practical application of information technology techniques to aid system engineers effectively explore large design spaces. We make use of heuristic search, visualization and data mining, the combination of which we have implemented wtihin a risk management tool in use at JPL and NASA.
We describe here our project and plans to develop methods, software tools, and infrastructure tools to address challenges relating to geographically distributed software development. Specifically, this work is creating an infrastructure that supports applications working over distributed geographical and organizational domains and is using this infrastructure to develop a tool that supports project development using risk management and analysis techniques where the participants are not collocated.
NASA's Mars Science Laboratory (MSL) rover mission is planning to make use of advanced software technologies in order to support fulfillment of its ambitious science objectives. The mission plans to adopt the Mission Data System (MDS) as the mission software architecture, and plans to make significant use of on-board autonomous capabilities for the rover software.
A risk-based decision-making methodology conceived and developed at JPL and NASA has been used to aid in decision making for spacecraft technology assessment, adoption, development and operation. It takes a risk-centric perspective, through which risks are used as a reasoning step to interpose between mission objectives and risk mitigation measures.
In planning a complex system's development there can be many options to improve its reliability. Typically their sum total cost exceeds the budget available, so it is necessary to select judiciously from among them. Reliability models can be employed to calculate the cost and reliability implications of a candidate selection.
A risk-based decision-making process conceived of and developed at JPL and NASA, has been used to help plan and guide novel technology applications for use on spacecraft. These applications exemplify key challenges inherent in multi-disciplinary design of novel technologies deployed in mission-critical settings. 1) Cross-disciplinary concerns are numerous (e.g., spacecraft involve navigation, propulsion, telecommunications). These concems are cross-coupled and interact in multiple ways (e.g., electromagnetic interference, heat transfer). 2) Time and budget pressures constrain development, operational resources constrain the resulting system (e.g., mass, volume, power). 3) Spacecraft are critical systems that must operate correctly the first time in only partially understood environments, with no chance for repair. 4) Past experience provides only a partial guide: New mission concepts are enhanced and enabled by new technologies, for which past experience is lacking. The decision-making process rests on quantitative assessments of the relationships between three classes of information - objectives (the things the system is to accomplish and constraints on its operation and development), risks (whose occurrence detracts from objectives), and mitigations (options for reducing the likelihood and or severity of risks). The process successfully guides experts to pool their knowledge, using custom-built software to support information gathering and decision-making.
Many organizations look to research to yield new and improved products and practices. Connecting practitioners who have the need for research results to the researchers producing those results is important to guiding research and utilizing its results. Likewise, connecting researchers working on related topics to one another, and connecting practitioners with related needs to one another, is important to establishing communities of shared interests. We present an approach that helps identify fruitful such connections.
The premise of this paper is taht there is a useful analogy between evaluation of proposed problem solutions and evaluation of requirements engineering research itself. Both of these application areas face the challenges of evaluation early in the lifecycle, of the need to consider a wide variety of factors, and of the need to combine inputs from multiple stakeholders in making thse evaluation and subsequent decisions.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
At JPL and NASA, a process has been developed to perform life cycle risk management. This process requires users to identify: goals and objectives to be achieved (and their relative priorities), the various risks to achieving those goals and objectives, and options for risk mitigation (prevention, detection ahead of time, and alleviation). Risks are broadly defined to include the risk of failing to design a system with adequate performance, compatibility and robustness in addition to more traditional implementation and operational risks. The options for mitigating these different kinds of risks can include architectural and design choices, technology plans and technology back-up options, test-bed and simulation options, engineering models and hardware/software development techniques and other more traditional risk reduction techniques.
Many current-day software design tools offer some variant of statechart notation for system specification. We, like others, have built an automatic translator from (a subset of) statecharts to a model checker, for use to validate behavioral requirements. Our translator is designed to be flexible. This allows us to quickly adjust the translator to variants of statechart semantics, including problem-specific notational conventions that designers employ. Our system demonstration will be of interest to the following two communities: (1) Potential end-users: Our demonstration will show translation from statecharts created in a commercial UML tool (Rational Rose) to Promela, the input language of Holzmann's model checker SPIN. The translation is accomplished automatically. To accommodate the major variants of statechart semantics, our tool offers user-selectable choices among semantic alternatives. Options for customized semantic variants are also made available. The net result is an easy-to-use tool that operates on a wide range of statechart diagrams to automate the pathway to model-checking input. (2) Other researchers: Our translator embodies, in one tool, ideas and approaches drawn from several sources. Solutions to the major challenges of statechart-to-model-checker translation (e.g., determining which transition(s) will fire, handling of concurrent activities) are retired in a uniform, fully mechanized, setting. The way in which the underlying architecture of the translator itself facilitates flexible and customizable translation will also be evident.
This paper presents the aspects of language programming transformations that were unknown in the early 1980's.
We have introduced and used significant automation during the verification and validation (V&V) of a spacecraft's autonomous planner. This paper describes the problem we faced, the solution we employed, and the applicability of our approach in a general V&V setting.
There is a trend towards the increased use of automation in V&V. Automation can yield savings in time and effort. For critical systems, where thorough V&V is required, these savings can be substantial. We describe a progression from pilot studies to development and use of V&V automation. We used pilot studies to ascertain opportunities for, and suitability of, automating various analyses whose results would contribute to V&V. These studies culminated in the development of an automatic generator of automated test oracles. This was then applied and extended in the course of testing an Al planning system that is a key component of an autonomous spacecraft.
Lightweight formal methods promise to yield modest analysis results in an extremely rapid manner. To fulfill this promise, they must be able to work with existing information sources, be able to analyze for manifestly desirable properties, be hightly atuomated (especially if dealing with voluminous amounts of information), and able readily customizable and flexible in the face of emerging needs and understanding.