Anvaya: An Algorithm and Case-Study on Improving the Goodness of Software Process Models generated by Mining Event-Log Data in Issue Tracking System

TL;DR

This paper presents Anvaya, an algorithm that improves process model quality from issue tracking logs by clustering traces to produce clearer, more analyzable models, demonstrated on the Firefox project.

Contribution

It introduces a clustering-based approach using K-Medoid with LCS and DTW metrics to enhance process model goodness from ITS logs, with an automated clustering algorithm.

Findings

Clustering improves process model fitness and reduces complexity.

Analysis of clusters reveals insights into bug lifecycle behaviors.

Automated clustering enhances process model analysis efficiency.

Abstract

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.