Abstract: This paper describes the model that captures a variety of features important for code readability: visual, structural and textual. Code readability can be defined as a measure of how easy it is to understand the logical context of the source code, work on it collaboratively and maintain the same. Successful classification of code as readable or unreadable is a prevalent problem in today AI’s world. By the automatic discovery of unreadable code, we could significantly reduce the time needed for software development and maintenance, enforce best practices and potentially discover bugs in the code. Current solutions for the measurement of code readability are still unsatisfactory from the aspect of accuracy. To build an accurate code readability model, an appropriate dataset is needed. All existing solutions use a set of structural and/or textual features, but none of them use a visual component. Accordingly, this work represents an improvement in the described problem, introducing visual component. The visual features in our model serve to express the visual focus of the person while reading a piece of code with the hypothesis that a person will pay more attention to the more complex and penitentially less readable parts of the programming code. On the other side, the structural features are used to express the key programming concepts of the target programming language and describe the impact of code’s general structure on its readability, as well as the impact of some rarely used concepts in contrast to often used ones. Finally, the textual features, extracted from comments and identifiers, describe the semantics of software's logic and in that way contribute to a higher degree of code comprehension and readability.