  In software industries, individuals at different levels from customer to an
engineer apply diverse mechanisms to detect to which class a particular bug
should be allocated. Sometimes while a simple search in Internet might help, in
many other cases a lot of effort is spent in analyzing the bug report to
classify the bug. So there is a great need of a structured mining algorithm -
where given a crash log, the existing bug database could be mined to find out
the class to which the bug should be allocated. This would involve Mining
patterns and applying different classification algorithms. This paper focuses
on the feature extraction, noise reduction in data and classification of
network bugs using probabilistic Na\"ive Bayes approach. Different event models
like Bernoulli and Multinomial are applied on the extracted features. When new,
unseen bugs are given as input to the algorithms, the performance comparison of
different algorithms is done on the basis of accuracy and recall parameters.
