Scholarlink

The problem of assessing the level of the design work complexity in the IT industry is of vital importance. This is due to the fact that design in this industry involves the consolidation of a wide variety of knowledge sectors: programming, network technologies, engineering infrastructure, etc. From such diversity follows the absence of generally accepted methods for quantitative assessment of the complexity and volume of design work based on the most general requirements for the designed IT system. In this research, a method for quantitative indicative assessment of the complexity of one of the main phases of IT system design is proposed: architectural planning. Using a seven-factor structure of fuzzy linguistic variables, a fuzzy possibility model was developed based on expert knowledge and experience. This model allows for quantitative assessment and forecasting of the complexity of tasks in the architecture of information technology systems.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Contribution of Fuzzy-Possibility Approach to Assessing the Complexity Level of IT-Systems Design

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Igor Kimyaev,
  • Alexander Spesivtsev,
  • Vasily Spesivtsev,
  • Nikolay Shilov

摘要

The problem of assessing the level of the design work complexity in the IT industry is of vital importance. This is due to the fact that design in this industry involves the consolidation of a wide variety of knowledge sectors: programming, network technologies, engineering infrastructure, etc. From such diversity follows the absence of generally accepted methods for quantitative assessment of the complexity and volume of design work based on the most general requirements for the designed IT system. In this research, a method for quantitative indicative assessment of the complexity of one of the main phases of IT system design is proposed: architectural planning. Using a seven-factor structure of fuzzy linguistic variables, a fuzzy possibility model was developed based on expert knowledge and experience. This model allows for quantitative assessment and forecasting of the complexity of tasks in the architecture of information technology systems.