Reference-free evaluation of LLM-generated code is essential when execution-based testing is unavailable or costly. We compare two paradigms: explicit LLM-as-a-Judge scoring, which assigns a quality score to a solution, and log-probability scoring, which uses log P𝜃(code ∣ task) as an instruction-free signal.Across HumanEval-X, we find that the two approaches capture qualitatively different aspects of code correctness. Explicit judges — particularly larger models — perform strongly on generated code, reflecting their ability to reason about task-solution alignment, but fail to distinguish correct solutions from minimally mutated ones. Log-probability exhibits the opposite pattern: weaker performance on generated code, but consistent pairwise separation of canonical from mutated solutions.These results reveal a discrimination-ranking dissociation and show that the two paradigms provide complementary, non-interchangeable signals: explicit judges capture semantic correctness, while log-probability captures local structural consistency.

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.