Abstract
Modeling languages are needed to describe the conceptual construct underlying software. Several modeling languages have been proposed during the last decades. Cognitive complexity is one of the common problems in designing modeling languages. Users have to split their attention and cognitive resources between two different tasks when working with complex language: solving the problem and understanding the elements composing the language. Several researches have been accomplished to evaluate cognitive complexity of modeling languages, among them, metric based and empirical approaches aremore important and convenient than others. In this paper, we compared these two methods. Results show that there is no significant relation between outputs generated by these approaches.
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