  The integrity and precision of nuclear data are crucial for a broad spectrum
of applications, from national security and nuclear reactor design to medical
diagnostics, where the associated uncertainties can significantly impact
outcomes. A substantial portion of uncertainty in nuclear data originates from
the subjective biases in the evaluation process, a crucial phase in the nuclear
data production pipeline. Recent advancements indicate that automation of
certain routines can mitigate these biases, thereby standardizing the
evaluation process, reducing uncertainty and enhancing reproducibility. This
article contributes to developing a framework for automated evaluation
techniques testing, emphasizing automated fitting methods that do not require
the user to provide any prior information. This approach simplifies the process
and reduces the manual effort needed in the initial evaluation stage. It
highlights the capability of the framework to validate and optimize
subroutines, targeting the performance analysis and optimization of the fitting
procedure using high-fidelity synthetic data (labeled experimental data) and
the concept of a fully controlled computational experiment. An error metric is
introduced to provide a clear and intuitive measure of the fitting quality by
quantifying the accuracy and performance across the specified energy. This
metric sets a scale for comparison and optimization of routines or
hyperparameter selection, improving the entire evaluation process methodology
and increasing reproducibility and objectivity.
