Abstract. Neither systems engineering nor process improvement is new. Since 1992, INCOSE papers and others have been reporting success in documenting and improving processes. A considerable body of process improvement literature is available, particularly related to improvement of software development processes. Even systems engineering process improvement is gaining in popularity, judging from the increasing number of INCOSE papers detailing various efforts. Yet the nature of systems engineering poses challenges over and above those seen in other process improvement efforts. This paper focuses on identifying and resolving typical barriers to improving the systems engineering process.

Neither systems engineering nor process improvement is new. Since 1992, INCOSE papers and others have been reporting success in documenting and improving processes. A considerable body of process improvement literature is available, particularly related to improvement of software development processes. Even systems engineering process improvement is gaining in popularity, judging from the increasing number of INCOSE papers detailing various efforts. Yet the nature of systems engineering poses challenges over and above those seen in other process improvement efforts. This paper focuses on identifying and resolving typical barriers to improving the systems engineering process. Repeat

This paper describes a concept for a parameter-based representation (PBR) to model complex systems. The representation combines features from both process and object-oriented representations, including IDEFO, data and control flow diagrams, entity relationship attribute (ERA) diagrams, and parameter dependency diagrams. PBR applies and extends many concepts that are widely used in software engineering to system-level design.The parameter-based approach provides enhanced capability over existing representations because it represents the multiple attributes of a system-including the performance, physical, design, and process parametersand explicitly defines the relationships between them. As part of the system design, the parameters are logically partitioned into input, output, and mechanism objects. The relationships between the parameters are captured by behavioral, structural, and class representations. Because PBR can represent explicit mathematical relationships between system parameters, it can also be used to develop an executable model of the system, The PBR approach assists the systems designer in performing sensitivity and trade-off analysis, assessing the impact of design decisions and changes on system performance, and maintaining design traceability.

This paper describes some of the work products used or generated during the development of a softwareintensive system by integrated product teams (IPTs) working on the system elements. An information modeling approach represents the relationships among the information entities involved in the system-level management, design, verification, integration, and testing activities: the model has been extended to the subsystem, configuration item, and component levels. The purpose of the information model is to provide developers with additional knowledge about the work products composing the information flows defined in a process model. This additional knowledge makes it easier to adopt the process model, tailor it for use on a project, and understand the roles of the IPTs and their members.

There is a pressing need for research into the use of machine interrogable models for requirements analysis, design, integration, testing, and management at all levels of systems and software engineering. This paper describes a candidate framework for research into model-driven systems engineering. The framework is a recently published process for integrated systems and software-level engineering of softwareintensive systems, described in terms of management and development activities and associated information. To show how the process can serve as a research framework, this paper selects a subset of the activities and information flows, discusses how modeling relates to them, and raises some example issues.