Reframing Ai-Assisted Programming: Measurement Chains and Validation Design

Abstract

Progress in AI-assisted programming depends on more than accumulating favorable results. This critical synthesis connects hierarchical debugging that closes the gap between generated code and executable correctness with comparative analysis of observable coding patterns produced by people and machines and asks how measurement choices, boundary conditions, and decision costs shape the interpretation of both. The analysis combines two focal publications with 12 previously verified sources and organizes the evidence around programmer behavior, error localization, execution feedback, repair hierarchy, and evaluation leakage. Rather than pooling incompatible outcomes, it compares research questions, representations, controls, and validation envelopes. Across the evidence base, the decisive issue is alignment: programmer behavior shapes what is observed, error localization shapes how it is compared, and evaluation leakage governs whether the conclusion can be transferred. Uncertainty is most informative when reported as part of the result rather than treated as a postscript. On this basis, the review proposes an auditable pathway from focal mechanism to application claim, with explicit checkpoints for calibration, external validity, and responsible interpretation.