  A key aspect in the determination of stellar properties is the comparison of
observational constraints with predictions from stellar models. Asteroseismic
Inference on a Massive Scale (AIMS) is an open source code that uses Bayesian
statistics and a Markov Chain Monte Carlo approach to find a representative set
of models that reproduce a given set of classical and asteroseismic
constraints. These models are obtained by interpolation on a pre-calculated
grid, thereby increasing computational efficiency. We test the accuracy of the
different operational modes within AIMS for grids of stellar models computed
with the Li\`ege stellar evolution code (main sequence and red giants) and
compare the results to those from another asteroseismic analysis pipeline,
PARAM. Moreover, using artificial inputs generated from models within the grid
(assuming the models to be correct), we focus on the impact on the precision of
the code when considering different combinations of observational constraints
(individual mode frequencies, period spacings, parallaxes, photospheric
constraints,...). Our tests show the absolute limitations of precision on
parameter inferences using synthetic data with AIMS, and the consistency of the
code with expected parameter uncertainty distributions. Interpolation testing
highlights the significance of the underlying physics to the analysis
performance of AIMS and provides caution as to the upper limits in parameter
step size. All tests demonstrate the flexibility and capability of AIMS as an
analysis tool and its potential to perform accurate ensemble analysis with
current and future asteroseismic data yields.
