  Convolutional network-error correcting codes (CNECCs) are known to provide
error correcting capability in acyclic instantaneous networks within the
network coding paradigm under small field size conditions. In this work, we
investigate the performance of CNECCs under the error model of the network
where the edges are assumed to be statistically independent binary symmetric
channels, each with the same probability of error $p_e$($0\leq p_e<0.5$). We
obtain bounds on the performance of such CNECCs based on a modified generating
function (the transfer function) of the CNECCs. For a given network, we derive
a mathematical condition on how small $p_e$ should be so that only single edge
network-errors need to be accounted for, thus reducing the complexity of
evaluating the probability of error of any CNECC. Simulations indicate that
convolutional codes are required to possess different properties to achieve
good performance in low $p_e$ and high $p_e$ regimes. For the low $p_e$ regime,
convolutional codes with good distance properties show good performance. For
the high $p_e$ regime, convolutional codes that have a good \textit{slope} (the
minimum normalized cycle weight) are seen to be good. We derive a lower bound
on the slope of any rate $b/c$ convolutional code with a certain degree.
