The paper presents early results in the development of an approach to predictive modeling of human developer perceiving of code generated in question-answering scenarios with Large Language Model (LLM) applications. The study is focused on building a model for the description and prediction of human implicit behavior during evaluative judgment of generated code through evaluation of its consistency, correctness, and usefulness as subjective perceiving characteristics. We used Markov Decision Process (MDP) as a basic framework to describe the human developer and his/her perceiving. We consider two approaches (regression-based and classification-based) to identify MDP parameters so it can be used as an “artificial” developer for human-centered code evaluation. An experimental evaluation of the proposed approach was performed with survey data previously collected for several code generation LLMs in a question-answering scenario. The results show overall good performance of the proposed model in acceptance rate prediction (accuracy 0.82) and give promising perspectives for further development and application.