  Issue Tracking Systems (ITS) such as Bugzilla can be viewed as Process Aware
Information Systems (PAIS) generating event-logs during the life-cycle of a bug
report. Process Mining consists of mining event logs generated from PAIS for
process model discovery, conformance and enhancement. We apply process map
discovery techniques to mine event trace data generated from ITS of open source
Firefox browser project to generate and study process models. Bug life-cycle
consists of diversity and variance. Therefore, the process models generated
from the event-logs are spaghetti-like with large number of edges,
inter-connections and nodes. Such models are complex to analyse and difficult
to comprehend by a process analyst. We improve the Goodness (fitness and
structural complexity) of the process models by splitting the event-log into
homogeneous subsets by clustering structurally similar traces. We adapt the
K-Medoid clustering algorithm with two different distance metrics: Longest
Common Subsequence (LCS) and Dynamic Time Warping (DTW). We evaluate the
goodness of the process models generated from the clusters using complexity and
fitness metrics. We study back-forth \& self-loops, bug reopening, and
bottleneck in the clusters obtained and show that clustering enables better
analysis. We also propose an algorithm to automate the clustering process -the
algorithm takes as input the event log and returns the best cluster set.
