Data processing: software development – installation – and managem – Software program development tool
Reexamination Certificate
1999-02-26
2004-04-06
Ingberg, Todd (Department: 2124)
Data processing: software development, installation, and managem
Software program development tool
C700S097000
Reexamination Certificate
active
06718533
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to a programming tool for software development. More specifically, the present invention relates to a tool for developing and executing real-time control systems.
BACKGROUND OF THE INVENTION
Real-time system software is notoriously complex. Large projects must balance the special needs of real-time software—such as event processing, data sampling, and feedback control—with the needs of interacting teams of programmers, engineers, managers, and maintenance personnel. Successful projects require solid software architecture, an intuitive, graphical programming paradigm, and a well-developed reuse capability.
Building software for real-time control systems is a huge challenge. The software must coordinate actuators, sensors, and interactions with the real world. It must deal with complex and intelligent subsystems. Many systems are large applications developed by teams of programmers, engineers, architects, and managers. All team members must be able to both understand and contribute to the design. The demands for a modular, clean design are compounded by the special needs of control systems such as control and sequencing. Systems are built, simulated and tested incrementally for best results. Significant legacy code embodying years of experience is often required to be used. In the past, these control systems defied reorganization. Prior art development tools for such complex systems have simply not been able to keep pace with the demands of these complex systems.
Development tools built for other real-time applications-such as telecommunications or databases-are not well suited to the challenges of complex control systems. In particular, they are not well-suited for complex electromechanical systems. Generic programming languages and methodologies are useful for modeling. They carefully define the nuances of syntax and meta language. But often they do not truly accelerate development. In particular, prior art object-oriented modeling tools are useful for some systems as will be described below, but are not well suited for the needs of complex, real-time control systems.
Prior art mathematical simulation tools can also be useful. They allow you to build and test dynamic models of the physical processes in your system. However, in most systems they only apply to a small percentage of the total problem. They are not good programming systems, and thus cannot handle truly complex systems that require significant custom coding. In particular, mathematical simulation tools are useful for certain applications as will be described below, but are also not especially well-suited for the needs of complex, real-time control systems.
In general, two approaches have been used in the prior art to develop complex software systems: top-down design and bottom-up design. Top-down design means breaking the problem into smaller and smaller parts until the pieces can be implemented easily. Top-down design is very general and powerful; it can eventually solve most problems. It is also intuitive, it allows division of labor, and it encourages rapid prototyping through consideration of intermediate incomplete models of a complex system. It provides a high level view important to communication and understanding between team members and management.
Most people naturally think of complex systems in a top-down, object-oriented fashion. For instance, when you describe a car, you think of the engine, the frame, the transmission, and other systems as functional, complete objects that cooperate to make the whole complex system. However, the top-down design process has a serious flaw: it leads to unique solutions to every problem. Each top-down design subdivides the problem in a unique way. None of the resulting subsystems can be reused easily. A top-down design has been called “the process of making all your worst mistakes first.” A top-down designer building a car would start with a concept drawing, and break it into subsystems as best fit the problem. However, like a concept car, the result will contain many strange parts that all have to be painstakingly handcrafted.
Often, prior art object-oriented modeling tools are thought of as providing a top-down design approach because they provide an object model view of the world. In particular, various prior art object modeling tools work well for general problem areas, but are not optimal for complex, real-time control systems. For example, one object modeling tool available from Rational Corporation of Sunnyvale, Calif. is called “Rose.” Rose is based on the well-known Unified Language Modeling (UML) graphical development tool (a general tool). Rose provides event-driven processing for general problem areas. Another UML-based tool is “Rhapsody,” available from iLogix Corporation of Andover, Massachusetts that also addresses general problem areas. Another representative object modeling tool is “ObjecTime,” available from ObjectTime of Ottawa, Canada. ObjecTime is based on the real-time object-oriented modeling (ROOM) language and is especially suited for modeling telecommunications systems.
In general, these prior art object modeling tools are based primarily on event-driven processing because of the problems to which they are addressed. They are not well-suited for, and are unable to handle, sampled-data processing mainly because the problems they address do not require it. In general, these prior art object modeling tools do not provide sampled-data processing that is often critical in complex real-time systems. Specifically, these tools have no concept of matrices and are unable to implement detailed mathematical calculations nor evolve over time. These deficiencies mean that these tools may not be well-suited for certain real-time systems that require such complex mathematical simulations.
At the other end of the spectrum are bottom-up design tools. Bottom-up design is the process of synthesizing from preexisting components. A bottom-up design process for building a car would be to walk into an auto parts store and begin piecing together the vehicle from parts on the rack. Bottom-up design is great for reusing such parts. However, without the overall picture and goal in mind, it often does not result in a clean division of functionality. The bottom-up design process provides no overall view to communicate the goals and progress of a project between team members. Bottom-up design is important for reuse, however, and to achieve true leverage, software must be reused unmodified. By contrast, copying and modifying code creates many source versions that must be independently tested and maintained. Only true reuse generates the leverage that results in large dividends.
To be truly powerful, bottom-up design should support multiple levels of granularity. Small, functional components are easy to reuse, but do not provide large benefits. On the other hand, large-scale components provide large-scale benefits. The reuse of an entire subsystem as a component would accelerate greatly the next system's implementation. But complex components have complex interfaces and specific functions. The more complex the component, the harder it is to reuse. Clearly, to maximize the benefits of component-based design, components should be reusable at different levels.
Many of the prior art mathematical simulation tools often are thought of as providing a bottom-up design. As such, they provide advantages for certain narrow problem areas, but are not well-suited for modeling complex real-time systems. One simulation tool is “Simulink,” available from Math Works of Natick, Mass. Another simulation tool is “System Build,” available from Integrated Systems, Inc. of Sunnyvale, Calif. These prior art simulation tools (and others) are based upon a mathematical model of the world and use complex matrix libraries to simulate a model using matrices. These tools are useful for simulating complex processes over time where complex calculations are involved. These tools typically provide sampled-data processing that can simulate a sy
Chen Vincent W.
Joshi Rajive
Pardo-Castellote Gerardo
Schneider Stanley A.
Wang Howard H.
Beyer Weaver & Thomas LLP
Ingberg Todd
Real-Time Innovations, Inc.
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