Complexity of software is an important aspect of development and maintenance activities. A lot of research is dedicated to defining different software measures that capture what software complexity is. In most cases description of complexity is given to humans in forms of numbers. These quantitative measures reflect human-seen complexity with different levels of success.

The paper proposes a process of "translating" human-seen complexity into numbers. The process starts with an experiment that involves human beings and provides data with embedded knowledge about human perception of complexity. Data processing and analysis of data models built based on the data lead to discovery of simple rules which represent human perception of software complexity.