  We propose a general framework to scrutinize the performance of semi-analytic
codes of galaxy formation. The approach is based on the analysis of the outputs
from the model after a series of perturbations in the input parameters
controlling the baryonic physics. The perturbations are chosen in a way that
they do not change the results in the luminosity function or mass function of
the galaxy population. We apply this approach on a particular semi-analytic
model called GalICS. We chose to perturb the parameters controlling the
efficiency of star formation and the efficiency of supernova feedback. We keep
track of the baryonic and observable properties of the central galaxies in a
sample of dark matter halos with masses ranging from 10^{10} M_sol to 10^{13}
M_sol. We find very different responses depending on the halo mass. For small
dark matter halos its central galaxy responds in a highly predictable way to
small perturbation in the star formation and feedback efficiency. For massive
dark matter halos, minor perturbations in the input parameters can induce large
fluctuations on the properties of its central galaxy, at least $\sim 0.1$ in
(B-V) color or $\sim 0.5$ mag in U or r filter, in a seemingly random fashion.
We quantify this behavior through an objective scalar function we call
predictability. We argue that finding the origin of this behavior needs
additional information from other approximations and different semi-analytic
codes. Furthermore, the implementation of an scalar objective function, such as
the predictability, opens the door to quantitative benchmarking of
semi-analytic codes based on its numerical performance.
