Front-End Analysis: Generic and Nongeneric Models.

Abstract

Front-end analysis is described as an iterative process by means of which the requirements of a system may be made progressively more definitive. The importance of information to the process - whether it is obtained through an empirical study or from a generic data base - is stressed. The degree of detail with which system requirements may be specified depends on the level of information available at the time they are formulated. As specificity of system requirements increase, there may be a corresponding advance in the state of the system. The analysis process may be degraded by time-cost constraints, etc., the primary result being a reduction in the amount and quality of information needed by the analyst to determine system requirements and alternative action plans at adequate levels of specificity. It is agreed that this degrading effect of constraints may be minimized by an information procurement and management system that would make available to the analyst generic data. A number of models for front-end analysis that take advantage of generic data bases are presented within the context of training systems. Each is evaluated in terms of the gain in specificity of training requirements and instructional regimens that it may achieve relative to nongeneric analyses carried out under constrained and constraint-free conditions. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1980
Accession Number
ADA095106

Entities

People

  • B. E. Mulligan
  • J. F. Funaro

Tags

Communities of Interest

  • Biomedical
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Cost Analysis
  • Databases
  • Economic Analysis
  • Instructors
  • Naval Operations
  • Naval Personnel
  • Naval Training
  • Organizational Structure
  • Procurement
  • Reliability
  • Security
  • Students
  • Systems Engineering
  • Task Performance And Analysis
  • Test And Evaluation
  • Training
  • Training Devices

Readers

  • Artificial Intelligence
  • Computational Modeling and Simulation
  • Systems Analysis and Design