Abstract:
Issue Tracking Systems (ITS) such as Bugzilla can be viewed as Process
Aware Information Systems (PAIS) generating event-logs during the lifecycle
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 Sub sequence (LCS) and Dynamic
Time Warping (DTW). We evaluate the goodness of the process models
generated from the clusters using complexity and fitness metrics. Process
models generated after clustering have high degree of fitness and less structural
complexity and thus are easier to comprehend compared with the
process model generated from the entire event-log. 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.
